From b73e233acec5ad6c3aca7475288482f366f7a31f Mon Sep 17 00:00:00 2001 From: adamhrv Date: Fri, 5 Apr 2019 13:17:05 +0200 Subject: never say final, update uccs --- site/public/datasets/duke_mtmc/index.html | 159 +++++++++--------------------- 1 file changed, 45 insertions(+), 114 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 37de48ad..62e5d836 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -17,7 +17,7 @@

Datasets @@ -45,35 +45,41 @@

Website

Created

2014

Identities

Over 2,700

Used for

Face recognition, person re-identification

Created by

Computer Science Department, Duke University, Durham, US

Website

duke.edu

Duke Multi-Target, Multi-Camera Tracking Dataset (Duke MTMC)

[ PAGE UNDER DEVELOPMENT ]

Duke MTMC is a dataset of video recorded on Duke University campus during for the purpose of training, evaluating, and improving multi-target multi-camera tracking. The videos were recorded during February and March 2014 and cinclude

Includes a total of 888.8 minutes of video (ind. verified)

"We make available a new data set that has more than 2 million frames and more than 2,700 identities. It consists of 8×85 minutes of 1080p video recorded at 60 frames per second from 8 static cameras deployed on the Duke University campus during periods between lectures, when pedestrian traffic is heavy."

The dataset includes approximately 2,000 annotated identities appearing in 85 hours of video from 8 cameras located throughout Duke University's campus.

Duke MTMC pixel-averaged image of camera #5 is shown with the bounding boxes for each student drawn in white. (c) Adam Harvey

According to the dataset authors,

Duke MTMC

[ page under development ]

The Duke Multi-Target, Multi-Camera Tracking Dataset (MTMC) is a dataset of video recorded on Duke University campus during for the purpose of training, evaluating, and improving multi-target multi-camera tracking for surveillance. The dataset includes over 14 hours of 1080p video from 8 cameras positioned around Duke's campus during February and March 2014. Over 2,700 unique people are included in the dataset, which has become of the most widely used person re-identification image datasets.

The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy".

Who used Duke MTMC Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

- +

- To help understand how Duke MTMC Dataset has been used around the world for commercial, military and academic research; publicly available research citing Duke Multi-Target, Multi-Camera Tracking Project is collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal reserach projects at that location. + To help understand how Duke MTMC Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Duke Multi-Target, Multi-Camera Tracking Project was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location.

@@ -81,30 +87,19 @@

Academic

Commercial

Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

Who used Duke MTMC Dataset?

+ +

Dataset Citations

- This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. + The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms.

- -

@@ -112,93 +107,29 @@

Supplementary Information

- -

Dataset Citations

- The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. -

- -

Research Notes

Data Visualizations

Duke MTMC pixel-averaged image of camera #5 is shown with the bounding boxes for each student drawn in white. (c) Adam Harvey

TODO

"We make available a new data set that has more than 2 million frames and more than 2,700 identities. It consists of 8×85 minutes of 1080p video recorded at 60 frames per second from 8 static cameras deployed on the Duke University campus during periods between lectures, when pedestrian traffic is heavy." - 27a2fad58dd8727e280f97036e0d2bc55ef5424c
"This work was supported in part by the EPSRC Programme Grant (FACER2VM) EP/N007743/1, EPSRC/dstl/MURI project EP/R018456/1, the National Natural Science Foundation of China (61373055, 61672265, 61602390, 61532009, 61571313), Chinese Ministry of Education (Z2015101), Science and Technology Department of Sichuan Province (2017RZ0009 and 2017FZ0029), Education Department of Sichuan Province (15ZB0130), the Open Research Fund from Province Key Laboratory of Xihua University (szjj2015-056) and the NVIDIA GPU Grant Program." - ec9c20ed6cce15e9b63ac96bb5a6d55e69661e0b
"DukeMTMC aims to accelerate advances in multi-target multi-camera tracking. It provides a tracking system that works within and across cameras, a new large scale HD video data set recorded by 8 synchronized cameras with more than 7,000 single camera trajectories and over 2,000 unique identities, and a new performance evaluation method that measures how often a system is correct about who is where"
DukeMTMC is a new, manually annotated, calibrated, multi-camera data set recorded outdoors on the Duke University campus with 8 synchronized cameras. It consists of:
-
8 static cameras x 85 minutes of 1080p 60 fps video - More than 2,000,000 manually annotated frames - More than 2,000 identities - Manual annotation by 5 people over 1 year - More identities than all existing MTMC datasets combined - Unconstrained paths, diverse appearance
-
DukeMTMC Project -Ergys Ristani Ergys Ristani Ergys Ristani Ergys Ristani Ergys Ristani
change to heatmap overlay of each location
make fancy viz of foot trails with bbox and blurred persons
expand story
add google street view images of each camera location?
add actual head detections to header image with faces blurred
add 4 diverse example images with faces blurred
add map location of the brainwash cafe

People involved: -Ergys Ristani, Francesco Solera, Roger S. Zou, Rita Cucchiara, Carlo Tomasi.

Navigation:

Data Set - Downloads - Downloads - Dataset Extensions - Performance Measures - Tracking Systems - Publications - How to Cite - Contact

Welcome to the Duke Multi-Target, Multi-Camera Tracking Project.

DukeMTMC aims to accelerate advances in multi-target multi-camera tracking. It provides a tracking system that works within and across cameras, a new large scale HD video data set recorded by 8 synchronized cameras with more than 7,000 single camera trajectories and over 2,000 unique identities, and a new performance evaluation method that measures how often a system is correct about who is where. -DukeMTMC Data Set -Snapshot from the DukeMTMC data set.

DukeMTMC is a new, manually annotated, calibrated, multi-camera data set recorded outdoors on the Duke University campus with 8 synchronized cameras. It consists of:

8 static cameras x 85 minutes of 1080p 60 fps video - More than 2,000,000 manually annotated frames - More than 2,000 identities - Manual annotation by 5 people over 1 year - More identities than all existing MTMC datasets combined - Unconstrained paths, diverse appearance

News

05 Feb 2019 We are organizing the 2nd Workshop on MTMCT and ReID at CVPR 2019 - 25 Jul 2018: The code for DeepCC is available on github - 28 Feb 2018: OpenPose detections now available for download - 19 Feb 2018: Our DeepCC tracker has been accepted to CVPR 2018 - 04 Oct 2017: A new blog post describes ID measures of performance - 26 Jul 2017: Slides from the BMTT 2017 workshop are now available - 09 Dec 2016: DukeMTMC is now hosted on MOTChallenge

DukeMTMC Downloads

DukeMTMC dataset (tracking)

Dataset Extensions

Below is a list of dataset extensions provided by the community:

DukeMTMC-VideoReID (download) - DukeMTMC-reID (download) - DukeMTMC4REID - DukeMTMC-attribute

If you use or extend DukeMTMC, please refer to the license terms. -DukeMTMCT Benchmark

DukeMTMCT is a tracking benchmark hosted on motchallenge.net. Click here for the up-to-date rankings. Here you will find the official motchallenge-devkit used for evaluation by MOTChallenge. For detailed instructions how to submit on motchallenge you can refer to this link.

Trackers are ranked using our identity-based measures which compute how often the system is correct about who is where, regardless of how often a target is lost and reacquired. Our measures are useful in applications such as security, surveillance or sports. This short post describes our measures with illustrations, while for details you can refer to the original paper. -Tracking Systems

We provide code for the following tracking systems which are all based on Correlation Clustering optimization:

DeepCC for single- and multi-camera tracking [1] - Single-Camera Tracker (demo video) [2] - Multi-Camera Tracker (demo video, failure cases) [2] - People-Groups Tracker [3] - Original Single-Camera Tracker [4]

Notes

The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
+
a
https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/
+
a
"Attention-Aware Compositional Network for Person Re-identification". 2018. Source
+
a
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. source
+
a
"Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. Source
+
a b c
"Performance Measures and a Data Set for
+
a
"Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. Source
+
a
"Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. Source
+
a
"Horizontal Pyramid Matching for Person Re-identification". 2019. Source
+
a
"Re-Identification with Consistent Attentive Siamese Networks". 2018. Source
+

diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html index 3f2a698a..cda1cde5 100644 --- a/site/public/datasets/oxford_town_centre/index.html +++ b/site/public/datasets/oxford_town_centre/index.html @@ -46,9 +46,8 @@

Website

ox.ac.uk

Oxford Town Centre

[ page under development ]

The Oxford Town Centre dataset is a video of pedestrians in a busy downtown area in Oxford used for creating surveillance algorithms with potential applications in remote biometric analysis and non-cooperative face recognition. 1 The dataset was originally created to build algorithms that improve the stability of pedestrian detectors to provide more accurate head location estimates, leading to more accurate face recognition.

Oxford Town Centre dataset is unique in that it uses footage from a public CCTV camera that is designated for public safety. Since its publication in 2009, the Oxford Town Centre CCTV footage dataset, and all 2,200 people in the video, have been redistributed around the world for the purpose of surveillance research and development. There are over 80 verified research projects that have used the Oxford Town Centre dataset. The usage even extends to commercial organizations including Amazon, Disney, and OSRAM.

The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems. 1 The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009 2 the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among tensomes more.

The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating lack of consent or notice of participation in the research project.

Who used TownCentre?

@@ -110,8 +109,10 @@

Supplementary Information

Location

The street location of the camera used for the Oxford Town Centre dataset can be easily confirmed using only two visual clues in video: the GAP store and the main road source. The camera angle and field of view indicate that the camera was elevated and placed at the corner. The edge of the building is visible and there is a small white nylon strap and pigeon deterrent spikes visible on the upper perimeter of the building. The field of view indicates the camera uses a wide angle lens. Combined with the camera's stability and pigeon appearances in front of the camera at 1:24 and 3:29, these visual cues indicate that the camera was mounted outside on the corner of the building just above the deterrence spikes.

Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View and Oxford Town Centre dataset

Demo Videos Using Oxford Town Centre Dataset

The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs source. At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their research projects it is likely that they would also be able to access to this camera.

To discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera pointing in the same direction as the Oxford Town Centre dataset proving the camera can and has been rotated before.

As for the capture date, the text on the storefront display shows a sale happening from December 2nd – 7th indicating the capture date was between or just before those dates. The capture year is either 2008 or 2007 since prior to 2007 the Carphone Warehouse (photo, history) did not exist at this location. Since the sweaters in the GAP window display are more similar to those in a GAP website snapshot from November 2007, our guess is that the footage was obtained during late November or early December 2007. The lack of street vendors and slight waste residue near the bench suggests that is was probably a weekday after rubbish removal.

Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (map)

Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

Demo Videos Using Oxford Town Centre Dataset

Several researchers have posted their demo videos using the Oxford Town Centre dataset on YouTube:

TODO

make heatmap viz
add license info

a
Benfold, Ben and Reid, Ian. "Stable Multi-Target Tracking in Real-Time Surveillance Video". CVPR 2011. Pages 3457-3464.
-
"Guiding Visual Surveillance by Tracking Human Attention". 2009.
+
a
"Guiding Visual Surveillance by Tracking Human Attention". 2009.

-- cgit v1.2.3-70-g09d2 From 3dedfa97961c1c4569ee30fc9dc039ee46f8b19d Mon Sep 17 00:00:00 2001 From: adamhrv Date: Wed, 10 Apr 2019 23:06:55 +0200 Subject: update html --- site/public/about/legal/index.html | 19 +++++++++---------- site/public/datasets/brainwash/index.html | 2 ++ site/public/datasets/duke_mtmc/index.html | 7 +++---- site/public/datasets/oxford_town_centre/index.html | 4 ++-- 4 files changed, 16 insertions(+), 16 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/site/public/about/legal/index.html b/site/public/about/legal/index.html index 4af4ba05..a603f999 100644 --- a/site/public/about/legal/index.html +++ b/site/public/about/legal/index.html @@ -34,20 +34,17 @@

Legal / Privacy

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@@ -55,7 +52,7 @@

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Engaging in a prohibited use of the Services may result in civil, criminal, and/or administrative penalties, fines, or sanctions against the user and those assisting the user.

@@ -64,6 +61,8 @@

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diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html index 240ec499..10ee577c 100644 --- a/site/public/datasets/brainwash/index.html +++ b/site/public/datasets/brainwash/index.html @@ -117,6 +117,8 @@

change supp images to 2x2 grid with bboxes

add bounding boxes to the header image

remake montage with randomized images, with bboxes

add ethics link to Stanford

add optout info

a
"readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.
a
Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016.
diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 8ff4ef43..0d082c15 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -26,7 +26,7 @@
-
Duke MTMC is a dataset of surveillance camera footage of students on Duke University campus
Duke MTMC contains over 2 million video frames and 2,000 unique identities collected from 8 HD cameras at Duke University campus in March 2014 +
Duke MTMC is a dataset of surveillance camera footage of students on Duke University campus
Duke MTMC contains over 2 million video frames and 2,700 unique identities collected from 8 HD cameras at Duke University campus in March 2014

Published

2016
@@ -46,10 +46,9 @@
Website

duke.edu

Duke MTMC
-
[ page under development ]

The Duke Multi-Target, Multi-Camera Tracking Dataset (MTMC) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking is used for citywide dragnet surveillance systems such as those used throughout China by SenseTime 1 and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets 2. In fact researchers from both SenseTime 4 5 and SenseNets 3 used the Duke MTMC dataset for their research.

The Duke MTMC dataset is unique because it is the largest publicly available MTMC and person re-identification dataset and has the longest duration of annotated video. In total, the Duke MTMC dataset provides over 14 hours of 1080p video from 8 synchronized surveillance cameras. 6 It is among the most widely used person re-identification datasets in the world. The approximately 2,700 unique people in the Duke MTMC videos, most of whom are students, are used for research and development of surveillance technologies by commercial, academic, and even defense organizations.
-
A collection of 1,600 out of the 2,700 students captured into the Duke MTMC surveillance research dataset. These students were also included in the Duke MTMC Re-ID dataset extension. © megapixels.cc
The creation and publication of the Duke MTMC dataset in 2016 was originally funded by the U.S. Army Research Laboratory and the National Science Foundation 6. Since 2016 use of the Duke MTMC dataset images have been publicly acknowledged in research funded by or on behalf of the Chinese National University of Defense 7 8, IARPA and IBM 9, and U.S. Department of Homeland Security 10.
+
A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. © megapixels.cc
The creation and publication of the Duke MTMC dataset in 2016 was originally funded by the U.S. Army Research Laboratory and the National Science Foundation 6. Since 2016 use of the Duke MTMC dataset images have been publicly acknowledged in research funded by or on behalf of the Chinese National University of Defense 7 8, IARPA and IBM 9, and U.S. Department of Homeland Security 10.

The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy". 6 Camera 7 and 2 capture large groups of prospective students and children. Camera 5 was positioned to capture students as they enter and exit Duke University's main chapel. Each camera's location is documented below.

Duke MTMC camera locations on Duke University campus © megapixels.cc
Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc

Who used Duke MTMC Dataset?
@@ -118,7 +117,7 @@
a
"Attention-Aware Compositional Network for Person Re-identification". 2018. Source
a
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. source
a
"Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. Source
-
a b c
"Performance Measures and a Data Set for
+
a b c
"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. Source
a
"Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. Source
a
"Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. Source
a
"Horizontal Pyramid Matching for Person Re-identification". 2019. Source
diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html index cda1cde5..5379682c 100644 --- a/site/public/datasets/oxford_town_centre/index.html +++ b/site/public/datasets/oxford_town_centre/index.html @@ -46,8 +46,8 @@
Website

ox.ac.uk

Oxford Town Centre
-
The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems. 1 The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009 2 the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among tensomes more.
-
The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating lack of consent or notice of participation in the research project.
+
The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems. 1 The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009 2 the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more.
+
The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating they neither knew of or consented to participation in the research project.

Who used TownCentre?

-- cgit v1.2.3-70-g09d2 From 9b1e2709cbdb40eabb34d379df18e61c10e3737c Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Thu, 11 Apr 2019 18:44:21 +0200 Subject: add h3 --- megapixels/app/site/parser.py | 7 +++-- .../datasets/50_people_one_question/index.html | 2 +- site/public/datasets/brainwash/index.html | 4 +-- site/public/datasets/duke_mtmc/index.html | 8 ++--- site/public/datasets/hrt_transgender/index.html | 2 +- site/public/datasets/index.html | 36 ---------------------- site/public/datasets/lfw/index.html | 4 +-- site/public/datasets/msceleb/index.html | 4 +-- site/public/datasets/oxford_town_centre/index.html | 11 ++++--- site/public/datasets/uccs/index.html | 6 ++-- 10 files changed, 27 insertions(+), 57 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py index dc2a09f2..6b71e041 100644 --- a/megapixels/app/site/parser.py +++ b/megapixels/app/site/parser.py @@ -145,8 +145,11 @@ def parse_markdown(metadata, sections, s3_path, skip_h1=False): return ' {}'.format(key, footnote_count, key, index, index) key_regex = re.compile(key.replace('[', '\\[').replace('^', '\\^').replace(']', '\\]')) content = key_regex.sub(footnote_tag, content) - footnote_txt = footnote_txt.replace("{}_BACKLINKS".format(index), "".join(footnote_backlinks)) + footnote_txt = footnote_txt.replace("{}_BACKLINKS".format(key), "".join(footnote_backlinks)) + content += '

' + content += '

References

' content += footnote_txt + content += '

' return content @@ -254,7 +257,7 @@ def format_footnotes(footnotes, s3_path): continue key, note = footnote.split(': ', 1) footnote_index_lookup[key] = index - footnote_list.append('{}_BACKLINKS'.format(key, index) + markdown(note)) + footnote_list.append('{}_BACKLINKS'.format(key, key) + markdown(note)) index += 1 footnote_txt = '

' + '
'.join(footnote_list) + '

' diff --git a/site/public/datasets/50_people_one_question/index.html b/site/public/datasets/50_people_one_question/index.html index b27fa3e5..3b33f530 100644 --- a/site/public/datasets/50_people_one_question/index.html +++ b/site/public/datasets/50_people_one_question/index.html @@ -35,7 +35,7 @@

Purpose

Facial landmark estimation in the wild

Facial landmark estimation

Website

caltech.edu

diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html index 10ee577c..bd59f573 100644 --- a/site/public/datasets/brainwash/index.html +++ b/site/public/datasets/brainwash/index.html @@ -120,11 +120,11 @@

add ethics link to Stanford

add optout info

a
"readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.
+

References

a
"readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.
a
Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016.
a
Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.
a
Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.
-

diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 0d082c15..9bec47ed 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -35,10 +35,10 @@

2,000,000

Identities

1,812

2,700

Purpose

Person re-identification and multi-camera tracking

Person re-identification, multi-camera tracking

Created by

Computer Science Department, Duke University, Durham, US

@@ -112,7 +112,7 @@

Notes

The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
+

References

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
a
https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/
a
"Attention-Aware Compositional Network for Person Re-identification". 2018. Source
a
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. source
@@ -122,7 +122,7 @@
a
"Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. Source
a
"Horizontal Pyramid Matching for Person Re-identification". 2019. Source
a
"Re-Identification with Consistent Attentive Siamese Networks". 2018. Source
-

diff --git a/site/public/datasets/hrt_transgender/index.html b/site/public/datasets/hrt_transgender/index.html index 099dea4e..486b9122 100644 --- a/site/public/datasets/hrt_transgender/index.html +++ b/site/public/datasets/hrt_transgender/index.html @@ -38,7 +38,7 @@

Purpose

gender transition and facial recognition

Face recognition, gender transition biometrics

Website

faceaginggroup.com

diff --git a/site/public/datasets/index.html b/site/public/datasets/index.html index 6f87ff68..b01c1ac1 100644 --- a/site/public/datasets/index.html +++ b/site/public/datasets/index.html @@ -61,30 +61,6 @@

- -

- Market-1501 -

2015

Person re-identification

- - - -

Large-scale face recognition

1,000,000 images

100,000

- -

Oxford Town Centre @@ -109,18 +85,6 @@

- -

- VIPeR -

2007

Person re-identification

1,264 images

632

- - diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index cb487913..60a6bf0e 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -141,10 +141,10 @@

The word "future" appears 71 times

* denotes partial funding for related research

a b
http://vis-www.cs.umass.edu/lfw/results.html
+

References

a b
http://vis-www.cs.umass.edu/lfw/results.html
a
Jingtuo Liu, Yafeng Deng, Tao Bai, Zhengping Wei, Chang Huang. Targeting Ultimate Accuracy: Face Recognition via Deep Embedding. https://arxiv.org/abs/1506.07310
a b c
Lee, Justin. "PING AN Tech facial recognition receives high score in latest LFW test results". BiometricUpdate.com. Feb 13, 2017. https://www.biometricupdate.com/201702/ping-an-tech-facial-recognition-receives-high-score-in-latest-lfw-test-results
-

diff --git a/site/public/datasets/msceleb/index.html b/site/public/datasets/msceleb/index.html index fd64189c..cf3a654f 100644 --- a/site/public/datasets/msceleb/index.html +++ b/site/public/datasets/msceleb/index.html @@ -114,10 +114,10 @@

The dataset author spoke about his research at the CVPR conference in 2016 https://www.youtube.com/watch?v=Nl2fBKxwusQ

"readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.
+

References

"readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.
Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.
Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.
-

diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html index 5379682c..63dc52d4 100644 --- a/site/public/datasets/oxford_town_centre/index.html +++ b/site/public/datasets/oxford_town_centre/index.html @@ -29,10 +29,13 @@

Oxford Town Centre is a dataset of surveillance camera footage from Cornmarket St Oxford, England

The Oxford Town Centre dataset includes approximately 2,200 identities and is used for research and development of face recognition systems

a
Benfold, Ben and Reid, Ian. "Stable Multi-Target Tracking in Real-Time Surveillance Video". CVPR 2011. Pages 3457-3464.
+

References

a
Benfold, Ben and Reid, Ian. "Stable Multi-Target Tracking in Real-Time Surveillance Video". CVPR 2011. Pages 3457-3464.
a
"Guiding Visual Surveillance by Tracking Human Attention". 2009.
-

diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html index c9faac68..794e3e69 100644 --- a/site/public/datasets/uccs/index.html +++ b/site/public/datasets/uccs/index.html @@ -29,7 +29,7 @@

UnConstrained College Students is a dataset of long-range surveillance photos of students at University of Colorado in Colorado Springs

The UnConstrained College Students dataset includes 16,149 images and 1,732 identities of subjects on University of Colorado Colorado Springs campus and is used for making face recognition and face detection algorithms

a
Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.
+

References

a
Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.
a b
Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.
-

-- cgit v1.2.3-70-g09d2 From 828ab34ca5e01e03e055ef9e091a88cd516a6061 Mon Sep 17 00:00:00 2001 From: adamhrv Date: Mon, 15 Apr 2019 14:08:35 +0200 Subject: fix up duke --- site/assets/css/applets.css | 2 +- site/assets/css/css.css | 60 ++-- site/content/pages/datasets/duke_mtmc/index.md | 78 ++++-- site/content/pages/datasets/msceleb/index.md | 4 + site/content/pages/datasets/uccs/index.md | 17 +- site/public/about/assets/LICENSE/index.html | 58 ++++ site/public/about/attribution/index.html | 78 ++++++ site/public/about/index.html | 107 +++++++ site/public/about/legal/index.html | 108 +++++++ site/public/about/press/index.html | 59 ++++ .../datasets/50_people_one_question/index.html | 114 ++++++++ site/public/datasets/afad/index.html | 127 +++++++++ site/public/datasets/brainwash/index.html | 163 +++++++++++ site/public/datasets/caltech_10k/index.html | 124 +++++++++ site/public/datasets/celeba/index.html | 126 +++++++++ site/public/datasets/cofw/index.html | 179 ++++++++++++ site/public/datasets/duke_mtmc/index.html | 139 +++++++-- site/public/datasets/feret/index.html | 87 ++++++ site/public/datasets/hrt_transgender/index.html | 2 +- site/public/datasets/lfpw/index.html | 116 ++++++++ site/public/datasets/lfw/index.html | 2 +- site/public/datasets/market_1501/index.html | 132 +++++++++ site/public/datasets/msceleb/index.html | 4 +- site/public/datasets/oxford_town_centre/index.html | 29 +- site/public/datasets/pipa/index.html | 120 ++++++++ site/public/datasets/pubfig/index.html | 117 ++++++++ site/public/datasets/uccs/index.html | 274 ++++++++++++++++++ site/public/datasets/vgg_face2/index.html | 142 ++++++++++ site/public/datasets/viper/index.html | 122 ++++++++ .../public/datasets/youtube_celebrities/index.html | 113 ++++++++ site/public/index.html | 39 +++ site/public/info/index.html | 50 ++++ site/public/research/00_introduction/index.html | 101 +++++++ .../research/01_from_1_to_100_pixels/index.html | 139 +++++++++ .../research/02_what_computers_can_see/index.html | 310 +++++++++++++++++++++ site/public/research/index.html | 49 ++++ site/public/test/chart/index.html | 50 ++++ site/public/test/citations/index.html | 50 ++++ site/public/test/csv/index.html | 50 ++++ site/public/test/datasets/index.html | 50 ++++ site/public/test/face_search/index.html | 50 ++++ site/public/test/gallery/index.html | 68 +++++ site/public/test/index.html | 61 ++++ site/public/test/map/index.html | 50 ++++ site/public/test/name_search/index.html | 50 ++++ site/public/test/pie_chart/index.html | 50 ++++ 46 files changed, 3947 insertions(+), 73 deletions(-) create mode 100644 site/public/about/assets/LICENSE/index.html create mode 100644 site/public/about/attribution/index.html create mode 100644 site/public/about/index.html create mode 100644 site/public/about/legal/index.html create mode 100644 site/public/about/press/index.html create mode 100644 site/public/datasets/50_people_one_question/index.html create mode 100644 site/public/datasets/afad/index.html create mode 100644 site/public/datasets/brainwash/index.html create mode 100644 site/public/datasets/caltech_10k/index.html create mode 100644 site/public/datasets/celeba/index.html create mode 100644 site/public/datasets/cofw/index.html create mode 100644 site/public/datasets/feret/index.html create mode 100644 site/public/datasets/lfpw/index.html create mode 100644 site/public/datasets/market_1501/index.html create mode 100644 site/public/datasets/pipa/index.html create mode 100644 site/public/datasets/pubfig/index.html create mode 100644 site/public/datasets/uccs/index.html create mode 100644 site/public/datasets/vgg_face2/index.html create mode 100644 site/public/datasets/viper/index.html create mode 100644 site/public/datasets/youtube_celebrities/index.html create mode 100644 site/public/index.html create mode 100644 site/public/info/index.html create mode 100644 site/public/research/00_introduction/index.html create mode 100644 site/public/research/01_from_1_to_100_pixels/index.html create mode 100644 site/public/research/02_what_computers_can_see/index.html create mode 100644 site/public/research/index.html create mode 100644 site/public/test/chart/index.html create mode 100644 site/public/test/citations/index.html create mode 100644 site/public/test/csv/index.html create mode 100644 site/public/test/datasets/index.html create mode 100644 site/public/test/face_search/index.html create mode 100644 site/public/test/gallery/index.html create mode 100644 site/public/test/index.html create mode 100644 site/public/test/map/index.html create mode 100644 site/public/test/name_search/index.html create mode 100644 site/public/test/pie_chart/index.html (limited to 'site/public/datasets/duke_mtmc') diff --git a/site/assets/css/applets.css b/site/assets/css/applets.css index e48af7b1..ef9f09e2 100644 --- a/site/assets/css/applets.css +++ b/site/assets/css/applets.css @@ -9,7 +9,7 @@ min-height: 0; } .applet { - margin-bottom: 40px; + margin-bottom: 60px; transition: opacity 0.2s cubic-bezier(0,0,1,1); opacity: 0; } diff --git a/site/assets/css/css.css b/site/assets/css/css.css index a61a1875..58f4d490 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -181,27 +181,34 @@ h2 { font-weight: 400; font-size: 34px; line-height: 43px; - margin: 20px 0 20px; + margin: 20px auto 20px auto; padding: 0; transition: color 0.1s cubic-bezier(0,0,1,1); font-family: 'Roboto Mono', monospace; } h3 { - margin: 0 0 18px 0; - padding: 20px 0 0 0; + margin: 20px auto 10px auto; font-size: 28px; font-weight: 400; transition: color 0.1s cubic-bezier(0,0,1,1); font-family: 'Roboto Mono', monospace; } h4 { - margin: 0 0 10px 0; + margin: 6px auto 10px auto; padding: 0; font-size: 18pt; font-weight: 400; transition: color 0.1s cubic-bezier(0,0,1,1); font-family: 'Roboto Mono', monospace; } +h5 { + margin: 6px auto 10px auto; + padding: 0; + font-size: 14pt; + font-weight: 400; + transition: color 0.1s cubic-bezier(0,0,1,1); + font-family: 'Roboto Mono', monospace; +} .content h3 a { color: #888; text-decoration: none; @@ -256,13 +263,25 @@ section { margin-top: 30px; line-height: 36px; } -p { - margin: 0 10px 20px 0; +section p { + margin: 10px auto 20px auto; line-height: 2; - font-size: 18px; + font-size: 17px; font-weight: 300; color: #dedede; } +section ul { + margin: 10px auto 20px auto; + max-width: 720px; +} +section h1, section h2, section h3, section h4, section h5, section h6, section p{ + max-width: 720px; +} + +.content-dataset section:nth-child(2) p:first-child{ + font-size:19px; + color:red; +} p.subp{ font-size: 14px; } @@ -299,10 +318,9 @@ p.subp{ .right-sidebar { float: right; width: 240px; - margin-left: 20px; - padding-top: 10px; + margin: 75px 20px 20px 20px; padding-left: 20px; - border-left: 1px solid #444; + border-left: 1px solid #333; font-family: 'Roboto'; font-size: 14px; font-weight: 400; @@ -363,12 +381,14 @@ code { background: rgba(255,255,255,0.1); } pre { - margin: 0 0 40px 0; + margin: 0 auto; + max-width: 720px; border: 1px solid #666; border-radius: 2px; padding: 10px; display: block; background: #333; + overflow: auto } pre code { display: block; @@ -450,7 +470,17 @@ blockquote { .footnotes hr { display: none; } -ul.footnotes li, ul.footnotes p{ +ul.footnotes{ + max-width:720px; + margin:0 auto; + font-size:12px; +} +ul.footnotes li{ + font-size:12px; + list-style-type: decimal; + margin-bottom:12px; +} +ul.footnotes p{ font-size:12px; } .footnotes ol:before { @@ -1060,10 +1090,6 @@ a.footnote_shim { bottom: 5px; margin-right: 2px; } -ul.footnotes { - list-style-type: decimal; - margin-left: 30px; -} li p { margin: 0; padding: 0; display: inline; @@ -1072,6 +1098,8 @@ li p { /* citation browser */ .citationHeader { + max-width: 720px; + margin: 10px auto; padding-bottom: 10px } .download-btn{ diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index 8308eee7..28c586f9 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -18,17 +18,19 @@ authors: Adam Harvey ## Duke MTMC -The Duke Multi-Target, Multi-Camera Tracking Dataset (MTMC) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking is used for citywide dragnet surveillance systems such as those used throughout China by SenseTime[^sensetime_qz] and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets[^sensenets_uyghurs]. In fact researchers from both SenseTime[^sensetime1] [^sensetime2] and SenseNets[^sensenets_sensetime] used the Duke MTMC dataset for their research. +Duke MTMC (Multi-Target, Multi-Camera Tracking) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking algorithms are used for citywide dragnet surveillance systems such as those used throughout China by SenseTime[^sensetime_qz] and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets[^sensenets_uyghurs]. In fact researchers from both SenseTime[^sensetime1] [^sensetime2] and SenseNets[^sensenets_sensetime] used the Duke MTMC dataset for their research. -The Duke MTMC dataset is unique because it is the largest publicly available MTMC and person re-identification dataset and has the longest duration of annotated video. In total, the Duke MTMC dataset provides over 14 hours of 1080p video from 8 synchronized surveillance cameras.[^duke_mtmc_orig] It is among the most widely used person re-identification datasets in the world. The approximately 2,700 unique people in the Duke MTMC videos, most of whom are students, are used for research and development of surveillance technologies by commercial, academic, and even defense organizations. +In this investigation into the Duke MTMC dataset, we found that researchers at Duke Univesity in Durham, North Carolina captured over 2,000 students, faculty members, and passersby into one of the most prolific public surveillance research datasets that's used around the world by commercial and defense surveillance organizations. -![caption: A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. © megapixels.cc](assets/duke_mtmc_reid_montage.jpg) +Since it's publication in 2016, the Duke MTMC dataset has been used in over 100 studies at organizations around the world including SenseTime[^sensetime1] [^sensetime2], SenseNets[^sensenets_sensetime], IARPA and IBM[^iarpa_ibm], Chinese National University of Defense [^cn_defense1][^cn_defense2], US Department of Homeland Security[^us_dhs], Tencent, Microsoft, Microsft Asia, Fraunhofer, Senstar Corp., Alibaba, Naver Labs, Google and Hewlett-Packard Labs to name only a few. -The creation and publication of the Duke MTMC dataset in 2016 was originally funded by the U.S. Army Research Laboratory and the National Science Foundation[^duke_mtmc_orig]. Since 2016 use of the Duke MTMC dataset images have been publicly acknowledged in research funded by or on behalf of the Chinese National University of Defense[^cn_defense1][^cn_defense2], IARPA and IBM[^iarpa_ibm], and U.S. Department of Homeland Security[^us_dhs]. +The creation and publication of the Duke MTMC dataset in 2014 (published in 2016) was originally funded by the U.S. Army Research Laboratory and the National Science Foundation[^duke_mtmc_orig]. Though our analysis of the geographic locations of the publicly available research shows over twice as many citations by researchers from China (44% China, 20% United States). In 2018 alone, there were 70 research project citations from China. -The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy".[^duke_mtmc_orig] Camera 7 and 2 capture large groups of prospective students and children. Camera 5 was positioned to capture students as they enter and exit Duke University's main chapel. Each camera's location is documented below. +![caption: A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research and development dataset on . These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. Open Data Commons Attribution License.](assets/duke_mtmc_reid_montage.jpg) -![caption: Duke MTMC camera locations on Duke University campus © megapixels.cc](assets/duke_mtmc_camera_map.jpg) +The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy".[^duke_mtmc_orig]. Camera 5 was positioned to capture students as entering and exiting the university's main chapel. Each camera's location and approximate field of view. The heat map visualization shows the locations where pedestrians were most frequently annotated in each video from the Duke MTMC datset. + +![caption: Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.](assets/duke_mtmc_camera_map.jpg) ![caption: Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_cameras.jpg) @@ -39,23 +41,67 @@ The 8 cameras deployed on Duke's campus were specifically setup to capture stude {% include 'supplementary_header.html' %} +#### Funding + +Original funding for the Duke MTMC dataset was provided by the Army Research Office under Grant No. W911NF-10-1-0387 and by the National Science Foundation +under Grants IIS-10-17017 and IIS-14-20894. + +#### Video Timestamps + +The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop [time sync data](http://vision.cs.duke.edu/DukeMTMC/details.html#time-sync) provided by the researchers, it at least aligns the relative time. The [rainy weather](https://www.wunderground.com/history/daily/KIGX/date/2014-3-19?req_city=Durham&req_state=NC&req_statename=North%20Carolina&reqdb.zip=27708&reqdb.magic=1&reqdb.wmo=99999) on that day also contribute towards the likelihood of March 14, 2014.. + +=== columns 2 + +| Camera | Date | Start | End | +| --- | --- | --- | --- | +| Camera 1 | March 14, 2014 | 4:14PM | 5:43PM | +| Camera 2 | March 14, 2014 | 4:13PM | 4:43PM | +| Camera 3 | March 14, 2014 | 4:20PM | 5:48PM | +| Camera 4 | March 14, 2014 | 4:21PM | 5:54PM | + +=========== -### Notes +| Camera | Date | Start | End | +| --- | --- | --- | --- | +| Camera 5 | March 14, 2014 | 4:12PM | 5:43PM | +| Camera 6 | March 14, 2014 | 4:18PM | 5:43PM | +| Camera 7 | March 14, 2014 | 4:16PM | 5:40PM | +| Camera 8 | March 14, 2014 | 4:25PM | 5:42PM | + +=== end columns + + +### Opting Out + +If you attended Duke University and were captured by any of the 8 surveillance cameras positioned on campus in 2014, there is unfortunately no way to be removed. The dataset files have been distributed throughout the world and it would not be possible to contact all the owners for removal. Nor do the authors provide any options for students to opt-out, nor did they even inform students they would be used at test subjects for surveillance research and development in a project funded, in part, by the United States Army Research Office. + +#### Notes + +- The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812 -The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812 {% include 'cite_our_work.html' %} +If you use any data from the Duke MTMC please follow their [license](http://vision.cs.duke.edu/DukeMTMC/#how-to-cite) and cite their work as: + +

+@inproceedings{ristani2016MTMC,
+  title =        {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
+  author =       {Ristani, Ergys and Solera, Francesco and Zou, Roger and Cucchiara, Rita and Tomasi, Carlo},
+  booktitle =    {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking},
+  year =         {2016}
+}
+

### Footnotes [^sensetime_qz]: [^sensenets_uyghurs]: -[^sensenets_sensetime]: "Attention-Aware Compositional Network for Person Re-identification". 2018. [Source](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e) -[^sensetime1]: "End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. [source](https://www.semanticscholar.org/paper/End-to-End-Deep-Kronecker-Product-Matching-for-Shen-Xiao/947954cafdefd471b75da8c3bb4c21b9e6d57838) -[^sensetime2]: "Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. [Source](https://www.semanticscholar.org/paper/Person-Re-identification-with-Deep-Graph-Neural-Shen-Li/08d2a558ea2deb117dd8066e864612bf2899905b) -[^duke_mtmc_orig]: "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. [Source](https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c) -[^cn_defense1]: "Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. [Source](https://www.semanticscholar.org/paper/Tracking-by-Animation%3A-Unsupervised-Learning-of-He-Liu/e90816e1a0e14ea1e7039e0b2782260999aef786) -[^cn_defense2]: "Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. [Source](https://www.semanticscholar.org/paper/Unsupervised-Multi-Object-Detection-for-Video-Using-He-He/59f357015054bab43fb8cbfd3f3dbf17b1d1f881) -[^iarpa_ibm]: "Horizontal Pyramid Matching for Person Re-identification". 2019. [Source](https://www.semanticscholar.org/paper/Horizontal-Pyramid-Matching-for-Person-Fu-Wei/c2a5f27d97744bc1f96d7e1074395749e3c59bc8) -[^us_dhs]: "Re-Identification with Consistent Attentive Siamese Networks". 2018. [Source](https://www.semanticscholar.org/paper/Re-Identification-with-Consistent-Attentive-Siamese-Zheng-Karanam/24d6d3adf2176516ef0de2e943ce2084e27c4f94) \ No newline at end of file +[^sensenets_sensetime]: "Attention-Aware Compositional Network for Person Re-identification". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e), [PDF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.pdf) +[^sensetime1]: "End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/End-to-End-Deep-Kronecker-Product-Matching-for-Shen-Xiao/947954cafdefd471b75da8c3bb4c21b9e6d57838), [PDF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf) +[^sensetime2]: "Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Person-Re-identification-with-Deep-Graph-Neural-Shen-Li/08d2a558ea2deb117dd8066e864612bf2899905b) +[^duke_mtmc_orig]: "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. [SemanticScholar](https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c) +[^cn_defense1]: "Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Tracking-by-Animation%3A-Unsupervised-Learning-of-He-Liu/e90816e1a0e14ea1e7039e0b2782260999aef786) +[^cn_defense2]: "Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Unsupervised-Multi-Object-Detection-for-Video-Using-He-He/59f357015054bab43fb8cbfd3f3dbf17b1d1f881) +[^iarpa_ibm]: "Horizontal Pyramid Matching for Person Re-identification". 2019. [SemanticScholar](https://www.semanticscholar.org/paper/Horizontal-Pyramid-Matching-for-Person-Fu-Wei/c2a5f27d97744bc1f96d7e1074395749e3c59bc8) +[^us_dhs]: "Re-Identification with Consistent Attentive Siamese Networks". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Re-Identification-with-Consistent-Attentive-Siamese-Zheng-Karanam/24d6d3adf2176516ef0de2e943ce2084e27c4f94) \ No newline at end of file diff --git a/site/content/pages/datasets/msceleb/index.md b/site/content/pages/datasets/msceleb/index.md index 065ee75d..34f220f5 100644 --- a/site/content/pages/datasets/msceleb/index.md +++ b/site/content/pages/datasets/msceleb/index.md @@ -21,6 +21,10 @@ authors: Adam Harvey [ PAGE UNDER DEVELOPMENT ] +https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology + +https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm + {% include 'dashboard.html' %} {% include 'supplementary_header.html' %} diff --git a/site/content/pages/datasets/uccs/index.md b/site/content/pages/datasets/uccs/index.md index 5cd17fa8..f2451c30 100644 --- a/site/content/pages/datasets/uccs/index.md +++ b/site/content/pages/datasets/uccs/index.md @@ -30,6 +30,10 @@ Combined funding sources for the creation of the initial and final release of th In 2017 the UCCS face dataset was used for a defense and intelligence agency funded [face recognition challenge](http://www.face-recognition-challenge.com/) at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was used for the [2nd Unconstrained Face Detection and Open Set Recognition Challenge](https://erodner.github.io/ial2018eccv/) at the European Computer Vision Conference (ECCV) in Munich, Germany. Additional research projects that have used the UCCS dataset are included below in the list of verified citations. +UCCS is part of the IARAP Janus team https://vast.uccs.edu/project/iarpa-janus/ + +https://arxiv.org/abs/1708.02337 + {% include 'dashboard.html' %} {% include 'supplementary_header.html' %} @@ -104,20 +108,15 @@ If you attended University of Colorado Colorado Springs and were captured by the ### Ethics -Please direct any questions about the ethics of the dataset to the University of Colorado Colorado Springs [Ethics and Compliance Office](https://www.uccs.edu/compliance/) - -### Technical Details +- Please direct any questions about the ethics of the dataset to the University of Colorado Colorado Springs [Ethics and Compliance Office](https://www.uccs.edu/compliance/) +- For further technical information about the dataset, visit the [UCCS dataset project page](https://vast.uccs.edu/Opensetface). -For further technical information about the dataset, visit the [UCCS dataset project page](https://vast.uccs.edu/Opensetface). +### Downloads -## Under Development - -- adding more verified locations to map and charts -- add EXIF file to CDN +- Download EXIF data for UCCS photos: [uccs_camera_exif.csv](https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_camera_exif.csv) {% include 'cite_our_work.html' %} - ### Footnotes [^funding_sb]: Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013. diff --git a/site/public/about/assets/LICENSE/index.html b/site/public/about/assets/LICENSE/index.html new file mode 100644 index 00000000..0d3a7878 --- /dev/null +++ b/site/public/about/assets/LICENSE/index.html @@ -0,0 +1,58 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

+ + +

+ Datasets + About +

+ +

and include this license and attribution protocol within any derivative work.

If you publish data derived from MegaPixels, the original dataset creators should first be notified.

The MegaPixels dataset is made available under the Open Data Commons Attribution License (https://opendatacommons.org/licenses/by/1.0/) and for academic use only.

READABLE SUMMARY OF Open Data Commons Attribution License

You are free:

To Share: To copy, distribute and use the dataset + To Create: To produce works from the dataset + To Adapt: To modify, transform and build upon the database

As long as you:

Attribute: You must attribute any public use of the database, or works produced from the database, in the manner specified in the license. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database.

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/about/attribution/index.html b/site/public/about/attribution/index.html new file mode 100644 index 00000000..0a1b8e0f --- /dev/null +++ b/site/public/about/attribution/index.html @@ -0,0 +1,78 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

+ + +

+ Datasets + About +

+ +

Legal

ATTRIBUTION PROTOCOL

If you use the MegaPixels data or any data derived from it, please cite the original work as follows:

+@online{megapixels,
+ author = {Harvey, Adam. LaPlace, Jules.},
+ title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+ year = 2019,
+ url = {https://megapixels.cc/},
+ urldate = {2019-04-20}
+}
+

and include this license and attribution protocol within any derivative work.

If you publish data derived from MegaPixels, the original dataset creators should first be notified.

The MegaPixels dataset is made available under the Open Data Commons Attribution License (https://opendatacommons.org/licenses/by/1.0/) and for academic use only.

READABLE SUMMARY OF Open Data Commons Attribution License

You are free:

To Share: To copy, distribute and use the dataset +To Create: To produce works from the dataset +To Adapt: To modify, transform and build upon the database
+

As long as you:

Attribute: You must attribute any public use of the database, or works produced from the database, in the manner specified in the license. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database.
+

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/about/index.html b/site/public/about/index.html new file mode 100644 index 00000000..4a4ab3c6 --- /dev/null +++ b/site/public/about/index.html @@ -0,0 +1,107 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

+ + +

+ Datasets + About +

+ +

About MegaPixels

MegaPixels is an independent art and research project by Adam Harvey and Jules LaPlace that investigates the ethics, origins, and individual privacy implications of face recognition image datasets and their role in the expansion of biometric surveillance technologies.

The MegaPixels site is made possible with support from Mozilla

Adam Harvey

is Berlin-based American artist and researcher. His previous projects (CV Dazzle, Stealth Wear, and SkyLift) explore the potential for counter-surveillance as artwork. He is the founder of VFRAME (visual forensics software for human rights groups) and is a currently researcher in residence at Karlsruhe HfG.

ahprojects.com

Jules LaPlace

is an American technologist and artist also based in Berlin. He was previously the CTO of a digital agency in NYC and now also works at VFRAME, developing computer vision and data analysis software for human rights groups. Jules also builds experimental software for artists and musicians. +

asdf.us

The MegaPixels website is based on an earlier installation from 2017 and ongoing research and lectures (TedX, CPDP) about facial recognition datasets. Over the last several years this project has evolved into a large-scale interrogation of hundreds of publicly-available face and person analysis datasets.

MegaPixels aims to provide a critical perspective on machine learning image datsets, one that might otherwise escape academia and the industry funded artificial intelligence think tanks that are often supported by the same technology companies who have created many of the datasets presented on this site.

MegaPixels is an independent project, designed as a public resource for educators, students, journalists, and researchers. Each dataset presented on this site undergoes a thorough review of its images, intent, and funding sources. Though the goals are similar to publishing a public academic paper, MegaPixels is a website-first reserch project aligns closley with the goals of pre-print academic publications. As such we welcome feedback and ways to improve this site and the clarity of the research.

Because this project surfaces many funding issues with datasets (from datasets funded by the C.I.A. to the National Unviversity of Defense and Technology in China), it is important that we are transparent about own funding. The original MegaPixels installation in 2017 was built as a commission for and with support from Tactical Technology Collective and Mozilla. The bulk of the research and web-development during 2018 - 2018 was supported by a grant from Mozilla. Continued development in 2019 is partially supported by a 1-year Reseacher-in-Residence grant from Karlsruhe HfG, lecture and workshop fees, and from commissions and sales from the Privacy Gift Shop.

Please get in touch if you are interested in supporting this project.

Team

Adam Harvey: Concept, research and analysis, design, computer vision
Jules LaPlace: Information and systems architecture, data management, web applications +You are free:

Contributing Researchers

Berit Gilma
Beth (aka Ms. Celeb)
Mathana Stender

Code and Libraries

Semantic Scholar for citation aggregation
Leaflet.js for maps
C3.js for charts
ThreeJS for 3D visualizations
PDFMiner.Six and Pandas for research paper data analysis

Please direct questions, comments, or feedback to mastodon.social/@adamhrv

Attribution

If you use MegaPixels or any data derived from it for your work, please cite our original work as follows:

+@online{megapixels,
+ author = {Harvey, Adam. LaPlace, Jules.},
+ title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+ year = 2019,
+ url = {https://megapixels.cc/},
+ urldate = {2019-04-20}
+}
+

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/about/legal/index.html b/site/public/about/legal/index.html new file mode 100644 index 00000000..9eb5dd5a --- /dev/null +++ b/site/public/about/legal/index.html @@ -0,0 +1,108 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

+ + +

+ Datasets + About +

+ +

Legal

MegaPixels.cc Terms and Privacy

Privacy

3rd Party Services

In order to provide certain features of the site, some 3rd party services are needed. Currently, the MegaPixels.cc site uses two 3rd party services: (1) Leaflet.js for the interactive map and (2) Digital Ocean Spaces as a content delivery network. Both services encrypt your requests to their server using HTTPS and neither service requires storing any cookies or authentication. However, both services will store files in your web browser's local cache (local storage) to improve loading performance. None of these local storage files are using for analytics, tracking, or any similar purpose.

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When you access the Service, we record your visit to the site in a server log file for the purposes of maintaining site security and preventing misuse. This includes your IP address and the header information sent by your web browser which includes the User Agent, referrer, and the requested page on our site.

Information We Share

We do not share or make public any information about individual site visitors, unless where required by law to the extent that server logs are only retained for a limited duration.

Information We Provide

We provide information for educational, journalistic, and research purposes. The published information on MegaPixels is made available under the Open Data Commons Attribution License (https://opendatacommons.org/licenses/by/1.0/) and for academic use only.

You are free:

To Share: To copy, distribute and use the dataset +To Create: To produce works from the dataset +To Adapt: To modify, transform and build upon the database
+

As long as you:

Attribute: You must attribute any public use of the database, or works produced from the database, in the manner specified in the license. For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database.
+

If you use the MegaPixels data or any data derived from it, please cite the original work as follows:

+@online{megapixels,
+ author = {Harvey, Adam. LaPlace, Jules.},
+ title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+ year = 2019,
+ url = {https://megapixels.cc/},
+ urldate = {2019-04-20}
+}
+

While every intention is made to publish only verifiable information, at times information may be edited, removed, or appended for clarity or correction. In no event will the operators of this site be liable for your use or misuse of the information provided.

We may terminate or suspend access to our Service immediately without prior notice or liability, for any reason whatsoever, including without limitation if you breach the Terms.

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Without prior written consent, you shall not:

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Governing Law

These Terms shall be governed and construed in accordance with the laws of Berlin, Germany, without regard to its conflict of law provisions.

Indemnity

Changes

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/about/press/index.html b/site/public/about/press/index.html new file mode 100644 index 00000000..7b0a3e87 --- /dev/null +++ b/site/public/about/press/index.html @@ -0,0 +1,59 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

+ + +

+ Datasets + About +

+ +

Press

Aug 22, 2018: "Transgender YouTubers had their videos grabbed to train facial recognition software" by James Vincent https://www.theverge.com/2017/8/22/16180080/transgender-youtubers-ai-facial-recognition-dataset

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/50_people_one_question/index.html b/site/public/datasets/50_people_one_question/index.html new file mode 100644 index 00000000..dfd8cbff --- /dev/null +++ b/site/public/datasets/50_people_one_question/index.html @@ -0,0 +1,114 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

50 People One Question Dataset

+ +

+ Datasets + About +

+ +

People One Question is a dataset of people from an online video series on YouTube and Vimeo used for building facial recogntion algorithms

People One Question dataset includes ... +

50 People 1 Question

[ page under development ]

Who used 50 People One Question Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how 50 People One Question Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing 50 People One Question was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/afad/index.html b/site/public/datasets/afad/index.html new file mode 100644 index 00000000..df14e7cd --- /dev/null +++ b/site/public/datasets/afad/index.html @@ -0,0 +1,127 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Asian Face Age Dataset

+ +

+ Datasets + About +

+ +

Asian Face Age Dataset

[ page under development ]

Who used Asian Face Age Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how Asian Face Age Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing The Asian Face Age Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research notes

The Asian Face Age Dataset (AFAD) is a new dataset proposed for evaluating the performance of age estimation, which contains more than 160K facial images and the corresponding age and gender labels. This dataset is oriented to age estimation on Asian faces, so all the facial images are for Asian faces. It is noted that the AFAD is the biggest dataset for age estimation to date. It is well suited to evaluate how deep learning methods can be adopted for age estimation. +Motivation
+
For age estimation, there are several public datasets for evaluating the performance of a specific algorithm, such as FG-NET [1] (1002 face images), MORPH I (1690 face images), and MORPH II[2] (55,608 face images). Among them, the MORPH II is the biggest public dataset to date. On the other hand, as we know it is necessary to collect a large scale dataset to train a deep Convolutional Neural Network. Therefore, the MORPH II dataset is extensively used to evaluate how deep learning methods can be adopted for age estimation [3][4].
+
However, the ethnic is very unbalanced for the MORPH II dataset, i.e., it has only less than 1% Asian faces. In order to evaluate the previous methods for age estimation on Asian Faces, the Asian Face Age Dataset (AFAD) was proposed.
+
There are 164,432 well-labeled photos in the AFAD dataset. It consist of 63,680 photos for female as well as 100,752 photos for male, and the ages range from 15 to 40. The distribution of photo counts for distinct ages are illustrated in the figure above. Some samples are shown in the Figure on the top. Its download link is provided in the "Download" section.
+
In addition, we also provide a subset of the AFAD dataset, called AFAD-Lite, which only contains PLACEHOLDER well-labeled photos. It consist of PLACEHOLDER photos for female as well as PLACEHOLDER photos for male, and the ages range from 15 to 40. The distribution of photo counts for distinct ages are illustrated in Fig. PLACEHOLDER. Its download link is also provided in the "Download" section.
+
The AFAD dataset is built by collecting selfie photos on a particular social network -- RenRen Social Network (RSN) [5]. The RSN is widely used by Asian students including middle school, high school, undergraduate, and graduate students. Even after leaving from school, some people still access their RSN account to connect with their old classmates. So, the age of the RSN user crosses a wide range from 15-years to more than 40-years old.
+
Please notice that this dataset is made available for academic research purpose only.
+

https://afad-dataset.github.io/

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html new file mode 100644 index 00000000..03331a2d --- /dev/null +++ b/site/public/datasets/brainwash/index.html @@ -0,0 +1,163 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Brainwash Dataset

+ +

+ Datasets + About +

+ +

Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco in 2014

The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms +

Brainwash Dataset

Brainwash is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe" 1 captured at 100 second intervals throught the entire day. Brainwash dataset was captured during 3 days in 2014: October 27, November 13, and November 24. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com. 2

Brainwash is not a widely used dataset but since its publication by Stanford University in 2015, it has notably appeared in several research papers from the National University of Defense Technology in Changsha, China. In 2016 and in 2017 researchers there conducted studies on detecting people's heads in crowded scenes for the purpose of surveillance. 3 4

If you happen to have been at Brainwash cafe in San Francisco at any time on October 26, November 13, or November 24 in 2014 you are most likely included in the Brainwash dataset and have unwittingly contributed to surveillance research.

Who used Brainwash Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how Brainwash Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Brainwash Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

Supplementary Information

+ +

A visualization of 81,973 head annotations from the Brainwash dataset training partition. © megapixels.cc

An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains about 12,000 images. License: Open Data Commons Public Domain Dedication (PDDL)

49 of the 11,918 images included in the Brainwash dataset. License: Open Data Commons Public Domain Dedication (PDDL)

TODO

change supp images to 2x2 grid with bboxes
add bounding boxes to the header image
remake montage with randomized images, with bboxes
add ethics link to Stanford
add optout info

+ +

Cite Our Work

+ + If you use our data, research, or graphics please cite our work: + +

+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}

+ +

References

a
"readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.
+
a
Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016.
+
a
Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.
+
a
Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.
+

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/caltech_10k/index.html b/site/public/datasets/caltech_10k/index.html new file mode 100644 index 00000000..00b5e7fd --- /dev/null +++ b/site/public/datasets/caltech_10k/index.html @@ -0,0 +1,124 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Brainwash Dataset

+ +

+ Datasets + About +

+ +

Caltech 10K Faces Dataset

[ page under development ]

Who used Brainwash Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research notes

The dataset contains images of people collected from the web by typing common given names into Google Image Search. The coordinates of the eyes, the nose and the center of the mouth for each frontal face are provided in a ground truth file. This information can be used to align and crop the human faces or as a ground truth for a face detection algorithm. The dataset has 10,524 human faces of various resolutions and in different settings, e.g. portrait images, groups of people, etc. Profile faces or very low resolution faces are not labeled.

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/celeba/index.html b/site/public/datasets/celeba/index.html new file mode 100644 index 00000000..c4caef20 --- /dev/null +++ b/site/public/datasets/celeba/index.html @@ -0,0 +1,126 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

CelebA Dataset

+ +

+ Datasets + About +

+ +

CelebA is a dataset of people...

CelebA includes... +

CelebA Dataset

[ PAGE UNDER DEVELOPMENT ]

Who used CelebA Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how CelebA Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Large-scale CelebFaces Attributes Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

Research

"An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks" mentions use by sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their"
7dab6fbf42f82f0f5730fc902f72c3fb628ef2f0
principal responsibility is ensuring the safety, security and reliability of the nation's nuclear weapons NNSA ( National Nuclear Security Administration )

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/cofw/index.html b/site/public/datasets/cofw/index.html new file mode 100644 index 00000000..4851e256 --- /dev/null +++ b/site/public/datasets/cofw/index.html @@ -0,0 +1,179 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

COFW Dataset

+ +

+ Datasets + About +

+ +

Caltech Occluded Faces in the Wild

[ PAGE UNDER DEVELOPMENT ]

Who used COFW Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how COFW Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Caltech Occluded Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research notes

Years

1993-1996

Images

14,126

Identities

1,199

Origin

Web Searches

Funded by

ODNI, IARPA, Microsoft

COFW is "is designed to benchmark face landmark algorithms in realistic conditions, which include heavy occlusions and large shape variations" [Robust face landmark estimation under occlusion].

We asked four people with different levels of computer vision knowledge to each collect 250 faces representative of typical real-world images, with the clear goal of challenging computer vision methods. +The result is 1,007 images of faces obtained from a variety of sources.
+

Robust face landmark estimation under occlusion

Our face dataset is designed to present faces in real-world conditions. Faces show large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.g. food, hands, microphones, etc.). All images were hand annotated in our lab using the same 29 landmarks as in LFPW. We annotated both the landmark positions as well as their occluded/unoccluded state. The faces are occluded to different degrees, with large variations in the type of occlusions encountered. COFW has an average occlusion of over 23%. +To increase the number of training images, and since COFW has the exact same landmarks as LFPW, for training we use the original non-augmented 845 LFPW faces + 500 COFW faces (1345 total), and for testing the remaining 507 COFW faces. To make sure all images had occlusion labels, we annotated occlusion on the available 845 LFPW training images, finding an average of only 2% occlusion.
+

http://www.vision.caltech.edu/xpburgos/ICCV13/

This research is supported by NSF Grant 0954083 and by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2014-14071600012.
+

https://www.cs.cmu.edu/~peiyunh/topdown/

+ +

Biometric Trade Routes

+ +

+ To help understand how COFW Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Caltech Occluded Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the location markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org and then dataset usage verified and geolocated.

+ +

Supplementary Information

+ +

Dataset Citations

+ +

Who used COFW Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

TODO

- replace graphic

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 9bec47ed..ba32484a 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -27,7 +27,7 @@

Duke MTMC is a dataset of surveillance camera footage of students on Duke University campus

Duke MTMC contains over 2 million video frames and 2,700 unique identities collected from 8 HD cameras at Duke University campus in March 2014 -

Duke MTMC

The Duke Multi-Target, Multi-Camera Tracking Dataset (MTMC) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking is used for citywide dragnet surveillance systems such as those used throughout China by SenseTime 1 and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets 2. In fact researchers from both SenseTime 4 5 and SenseNets 3 used the Duke MTMC dataset for their research.

The Duke MTMC dataset is unique because it is the largest publicly available MTMC and person re-identification dataset and has the longest duration of annotated video. In total, the Duke MTMC dataset provides over 14 hours of 1080p video from 8 synchronized surveillance cameras. 6 It is among the most widely used person re-identification datasets in the world. The approximately 2,700 unique people in the Duke MTMC videos, most of whom are students, are used for research and development of surveillance technologies by commercial, academic, and even defense organizations.

A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. © megapixels.cc

The creation and publication of the Duke MTMC dataset in 2016 was originally funded by the U.S. Army Research Laboratory and the National Science Foundation 6. Since 2016 use of the Duke MTMC dataset images have been publicly acknowledged in research funded by or on behalf of the Chinese National University of Defense 7 8, IARPA and IBM 9, and U.S. Department of Homeland Security 10.

The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy". 6 Camera 7 and 2 capture large groups of prospective students and children. Camera 5 was positioned to capture students as they enter and exit Duke University's main chapel. Each camera's location is documented below.

Duke MTMC camera locations on Duke University campus © megapixels.cc

Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc

Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc

Duke MTMC (Multi-Target, Multi-Camera Tracking) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking algorithms are used for citywide dragnet surveillance systems such as those used throughout China by SenseTime 1 and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets 2. In fact researchers from both SenseTime 4 5 and SenseNets 3 used the Duke MTMC dataset for their research.

In this investigation into the Duke MTMC dataset, we found that researchers at Duke Univesity in Durham, North Carolina captured over 2,000 students, faculty members, and passersby into one of the most prolific public surveillance research datasets that's used around the world by commercial and defense surveillance organizations.

Since it's publication in 2016, the Duke MTMC dataset has been used in over 100 studies at organizations around the world including SenseTime 4 5, SenseNets 3, IARPA and IBM 9, Chinese National University of Defense 7 8, US Department of Homeland Security 10, Tencent, Microsoft, Microsft Asia, Fraunhofer, Senstar Corp., Alibaba, Naver Labs, Google and Hewlett-Packard Labs to name only a few.

The creation and publication of the Duke MTMC dataset in 2014 (published in 2016) was originally funded by the U.S. Army Research Laboratory and the National Science Foundation 6. Though our analysis of the geographic locations of the publicly available research shows over twice as many citations by researchers from China (44% China, 20% United States). In 2018 alone, there were 70 research project citations from China.

A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research and development dataset on . These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. Open Data Commons Attribution License.

The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy". 6. Camera 5 was positioned to capture students as entering and exiting the university's main chapel. Each camera's location and approximate field of view. The heat map visualization shows the locations where pedestrians were most frequently annotated in each video from the Duke MTMC datset.

Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.

Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc

Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc

Who used Duke MTMC Dataset?

@@ -110,18 +111,122 @@

Supplementary Information

Notes

The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812

References

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
+

Funding

Original funding for the Duke MTMC dataset was provided by the Army Research Office under Grant No. W911NF-10-1-0387 and by the National Science Foundation +under Grants IIS-10-17017 and IIS-14-20894.

Video Timestamps

The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least aligns the relative time. The rainy weather on that day also contribute towards the likelihood of March 14, 2014..

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Camera	Date	Start	End
Camera 1	March 14, 2014	4:14PM	5:43PM
Camera 2	March 14, 2014	4:13PM	4:43PM
Camera 3	March 14, 2014	4:20PM	5:48PM
Camera 4	March 14, 2014	4:21PM	5:54PM

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Camera	Date	Start	End
Camera 5	March 14, 2014	4:12PM	5:43PM
Camera 6	March 14, 2014	4:18PM	5:43PM
Camera 7	March 14, 2014	4:16PM	5:40PM
Camera 8	March 14, 2014	4:25PM	5:42PM

Opting Out

If you attended Duke University and were captured by any of the 8 surveillance cameras positioned on campus in 2014, there is unfortunately no way to be removed. The dataset files have been distributed throughout the world and it would not be possible to contact all the owners for removal. Nor do the authors provide any options for students to opt-out, nor did they even inform students they would be used at test subjects for surveillance research and development in a project funded, in part, by the United States Army Research Office.

Notes

The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812

+ +

Cite Our Work

+ + If you use our data, research, or graphics please cite our work: + +

+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}

+ +

If you use any data from the Duke MTMC please follow their license and cite their work as:

+@inproceedings{ristani2016MTMC,
+ title =        {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
+ author =       {Ristani, Ergys and Solera, Francesco and Zou, Roger and Cucchiara, Rita and Tomasi, Carlo},
+ booktitle =    {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking},
+ year =         {2016}
+}
+

References

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
a
https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/
-
a
"Attention-Aware Compositional Network for Person Re-identification". 2018. Source
-
a
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. source
-
a
"Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. Source
-
a b c
"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. Source
-
a
"Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. Source
-
a
"Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. Source
-
a
"Horizontal Pyramid Matching for Person Re-identification". 2019. Source
-
a
"Re-Identification with Consistent Attentive Siamese Networks". 2018. Source
+
a b
"Attention-Aware Compositional Network for Person Re-identification". 2018. SemanticScholar, PDF
+
a b
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. SemanticScholar, PDF
+
a b
"Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. SemanticScholar
+
a b
"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. SemanticScholar
+
a
"Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. SemanticScholar
+
a
"Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. SemanticScholar
+
a
"Horizontal Pyramid Matching for Person Re-identification". 2019. SemanticScholar
+
a
"Re-Identification with Consistent Attentive Siamese Networks". 2018. SemanticScholar

diff --git a/site/public/datasets/feret/index.html b/site/public/datasets/feret/index.html new file mode 100644 index 00000000..089cd351 --- /dev/null +++ b/site/public/datasets/feret/index.html @@ -0,0 +1,87 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

LFW

+ +

+ Datasets + About +

+ +

Published

2007

Images

13,233

Identities

5,749

Purpose

face recognition

Website

umass.edu

FacE REcognition Dataset (FERET)

[ page under development ]

{% include 'dashboard.html' %}

(ignore) RESEARCH below this line

Years: 1993-1996
Images: 14,126
Identities: 1,199
Origin: Fairfax, MD
Facial Recognition Evaluation (FERET) is develop, test, and evaluate face recognition algorithms
The goal of the FERET program was to develop automatic face recognition capabilities that could be employed to assist security, intelligence, and law enforcement personnel in the performance of their duties.
https://www.nist.gov/programs-projects/face-recognition-technology-feret

"The FERET database and evaluation procedure for face-recognition algorithms"

Images were captured using Kodak Ultra film
The facial images were collected in 11 sessions from August 1993 to December 1994. Conducted at George Mason University and at US Army Research Laboratory facilities,

FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results

"A release form is necessary because of the privacy laws in the United States."

Funding

The FERET program is sponsored by the U.S. Depart- ment of Defense’s Counterdrug Technology Development Program Office. The U.S. Army Research Laboratory (ARL) is the technical agent for the FERET program. ARL designed, administered, and scored the FERET tests. George Mason University collected, processed, and main- tained the FERET database. Inquiries regarding the FERET database or test should be directed to P. Jonathon Phillips.

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/hrt_transgender/index.html b/site/public/datasets/hrt_transgender/index.html index 486b9122..231a5271 100644 --- a/site/public/datasets/hrt_transgender/index.html +++ b/site/public/datasets/hrt_transgender/index.html @@ -27,7 +27,7 @@

TBD

TBD -

Published

2013

diff --git a/site/public/datasets/lfpw/index.html b/site/public/datasets/lfpw/index.html new file mode 100644 index 00000000..f734d332 --- /dev/null +++ b/site/public/datasets/lfpw/index.html @@ -0,0 +1,116 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

LFWP

+ +

+ Datasets + About +

+ +

Labeled Face Parts in The Wild

Who used LFWP?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how LFWP has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Labeled Face Parts in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

RESEARCH below this line

Release 1 of LFPW consists of 1,432 faces from images downloaded from the web using simple text queries on sites such as google.com, flickr.com, and yahoo.com. Each image was labeled by three MTurk workers, and 29 fiducial points, shown below, are included in dataset. LFPW was originally described in the following publication:
+
Due to copyright issues, we cannot distribute image files in any format to anyone. Instead, we have made available a list of image URLs where you can download the images yourself. We realize that this makes it impossible to exactly compare numbers, as image links will slowly disappear over time, but we have no other option. This seems to be the way other large web-based databases seem to be evolving.
+

https://neerajkumar.org/databases/lfpw/

This research was performed at Kriegman-Belhumeur Vision Technologies and was funded by the CIA through the Office of the Chief Scientist. https://www.cs.cmu.edu/~peiyunh/topdown/ (nk_cvpr2011_faceparts.pdf)
+

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 60a6bf0e..2d91b065 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -27,7 +27,7 @@

Labeled Faces in The Wild (LFW) is the first facial recognition dataset created entirely from online photos

It includes 13,456 images of 4,432 people's images copied from the Internet during 2002-2004 and is the most frequently used dataset in the world for benchmarking face recognition algorithms. -

Published

2007

diff --git a/site/public/datasets/market_1501/index.html b/site/public/datasets/market_1501/index.html new file mode 100644 index 00000000..72807efc --- /dev/null +++ b/site/public/datasets/market_1501/index.html @@ -0,0 +1,132 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Market 1501

+ +

+ Datasets + About +

+ +

Market-1501 is a dataset is collection of CCTV footage from Tsinghua University

The Market-1501 dataset includes 1,261 people from 5 HD surveillance cameras located on campus +

Market-1501 Dataset

[ PAGE UNDER DEVELOPMENT]

Who used Market 1501?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how Market 1501 has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Market 1501 Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research Notes

"MARS is an extension of the Market-1501 dataset. During collection, we placed six near synchronized cameras in the campus of Tsinghua university. There were Five 1,0801920 HD cameras and one 640480 SD camera. MARS consists of 1,261 different pedestrians whom are captured by at least 2 cameras. Given a query tracklet, MARS aims to retrieve tracklets that contain the same ID." - main paper
bbox "0065C1T0002F0016.jpg", "0065" is the ID of the pedestrian. "C1" denotes the first +camera (there are totally 6 cameras). "T0002" means the 2th tracklet. "F016" is the 16th frame +within this tracklet. For the tracklets, their names are accumulated for each ID; but for frames, +they start from "F001" in each tracklet.

@proceedings{zheng2016mars, +title={MARS: A Video Benchmark for Large-Scale Person Re-identification}, +author={Zheng, Liang and Bie, Zhi and Sun, Yifan and Wang, Jingdong and Su, Chi and Wang, Shengjin and Tian, Qi}, +booktitle={European Conference on Computer Vision}, +year={2016}, +organization={Springer} +}

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/msceleb/index.html b/site/public/datasets/msceleb/index.html index cf3a654f..be21280c 100644 --- a/site/public/datasets/msceleb/index.html +++ b/site/public/datasets/msceleb/index.html @@ -27,7 +27,7 @@

MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms

The MS Celeb dataset includes over 10,000,000 images and 93,000 identities of semi-public figures collected using the Bing search engine -

Microsoft Celeb Dataset (MS Celeb)

[ PAGE UNDER DEVELOPMENT ]

https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology

https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm

Who used Microsoft Celeb?

diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html index 63dc52d4..af020855 100644 --- a/site/public/datasets/oxford_town_centre/index.html +++ b/site/public/datasets/oxford_town_centre/index.html @@ -27,7 +27,7 @@

Oxford Town Centre is a dataset of surveillance camera footage from Cornmarket St Oxford, England

The Oxford Town Centre dataset includes approximately 2,200 identities and is used for research and development of face recognition systems -

Published

2009

@@ -115,16 +115,23 @@

Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (<a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">map</a>)

Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (map)

Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

Demo Videos Using Oxford Town Centre Dataset

Several researchers have posted their demo videos using the Oxford Town Centre dataset on YouTube:

Multi target tracking on Oxford Dataset
[Multi-pedestrian tracking (TownCentre dataset)]https://www.youtube.com/watch?v=nO-3EM9dEd4
Multiple object tracking with kalman tracker and sort
Multi target tracking on Oxford dataset
towncentre
VTD - towncenter.avi

Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (map)

Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

+ +

Cite Our Work

+ + If you use our data, research, or graphics please cite our work: + +

+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}

+ +

References

a
Benfold, Ben and Reid, Ian. "Stable Multi-Target Tracking in Real-Time Surveillance Video". CVPR 2011. Pages 3457-3464.
a
"Guiding Visual Surveillance by Tracking Human Attention". 2009.

diff --git a/site/public/datasets/pipa/index.html b/site/public/datasets/pipa/index.html new file mode 100644 index 00000000..780b3029 --- /dev/null +++ b/site/public/datasets/pipa/index.html @@ -0,0 +1,120 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

PIPA Dataset

+ +

+ Datasets + About +

+ +

People in Photo Albums (PIPA) is a dataset...

[ add subdescrition ] +

People in Photo Albums

[ PAGE UNDER DEVELOPMENT ]

Who used PIPA Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how PIPA Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing People in Photo Albums Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/pubfig/index.html b/site/public/datasets/pubfig/index.html new file mode 100644 index 00000000..2c8bd7b1 --- /dev/null +++ b/site/public/datasets/pubfig/index.html @@ -0,0 +1,117 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

PubFig

+ +

+ Datasets + About +

+ +

PubFig is a dataset...

[ add subdescrition ] +

PubFig

[ PAGE UNDER DEVELOPMENT ]

Who used PubFig?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how PubFig has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Public Figures Face Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html new file mode 100644 index 00000000..4a0dfb5e --- /dev/null +++ b/site/public/datasets/uccs/index.html @@ -0,0 +1,274 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

UCCS

+ +

+ Datasets + About +

+ +

UnConstrained College Students is a dataset of long-range surveillance photos of students at University of Colorado in Colorado Springs

UnConstrained College Students

[ page under development ]

UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs. According to the authors of two papers associated with the dataset, subjects were "photographed using a long-range high-resolution surveillance camera without their knowledge" 2. To create the dataset, the researchers used a Canon 7D digital camera fitted with a Sigma 800mm telephoto lens and photographed students 150–200m away through their office window. Photos were taken during the morning and afternoon while students were walking to and from classes. The primary uses of this dataset are to train, validate, and build recognition and face detection algorithms for realistic surveillance scenarios.

What makes the UCCS dataset unique is that it includes the highest resolution images of any publicly available face recognition dataset discovered so far (18MP), that it was captured on a campus without consent or awareness using a long-range telephoto lens, and that it was funded by United States defense and intelligence agencies.

Combined funding sources for the creation of the initial and final release of the dataset include ODNI (Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. 1 2

In 2017 the UCCS face dataset was used for a defense and intelligence agency funded face recognition challenge at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was used for the 2nd Unconstrained Face Detection and Open Set Recognition Challenge at the European Computer Vision Conference (ECCV) in Munich, Germany. Additional research projects that have used the UCCS dataset are included below in the list of verified citations.

UCCS is part of the IARAP Janus team https://vast.uccs.edu/project/iarpa-janus/

https://arxiv.org/abs/1708.02337

Who used UCCS?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how UCCS has been used around the world by commercial, military, and academic organizations; existing publicly available research citing UnConstrained College Students Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

Supplementary Information

+ +

Dates and Times

The images in UCCS were taken on 18 non-consecutive days during 2012–2013. Analysis of the EXIF data embedded in original images reveal that most of the images were taken on Tuesdays, and the most frequent capture time throughout the week was 12:30PM.

UCCS photos captured per weekday © megapixels.cc

UCCS photos captured per 10-minute intervals per weekday © megapixels.cc

UCCS photos taken in 2012

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Date	Photos
Feb 23, 2012	132
March 6, 2012	288
March 8, 2012	506
March 13, 2012	160
March 20, 2012	1,840
March 22, 2012	445
April 3, 2012	1,639
April 12, 2012	14
April 17, 2012	19
April 24, 2012	63
April 25, 2012	11
April 26, 2012	20

UCCS photos taken in 2013

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Date	Photos
Jan 28, 2013	1,056
Jan 29, 2013	1,561
Feb 13, 2013	739
Feb 19, 2013	723
Feb 20, 2013	965
Feb 26, 2013	736

Location

The location of the camera and subjects can confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The original papers also provides another clue: a picture of the camera inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the location on Google Maps

Location on campus where students were unknowingly photographed with a telephoto lens to be used for defense and intelligence agency funded research on face recognition. Image: Google Maps

3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps

Funding

The UnConstrained College Students dataset is associated with two main research papers: "Large Scale Unconstrained Open Set Face Database" and "Unconstrained Face Detection and Open-Set Face Recognition Challenge". Collectively, these papers and the creation of the dataset have received funding from the following organizations:

ONR (Office of Naval Research) MURI (The Department of Defense Multidisciplinary University Research Initiative) grant N00014-08-1-0638
Army SBIR (Small Business Innovation Research) grant W15P7T-12-C-A210
SOCOM (Special Operations Command) SBIR (Small Business Innovation Research) grant H92222-07-P-0020
National Science Foundation Grant IIS-1320956
ODNI (Office of Director of National Intelligence)
IARPA (Intelligence Advance Research Projects Activity) R&D contract 2014-14071600012

Opting Out

If you attended University of Colorado Colorado Springs and were captured by the long range surveillance camera used to create this dataset, there is unfortunately currently no way to be removed. The authors do not provide any options for students to opt-out nor were students informed they would be used for training face recognition. According to the authors, the lack of any consent or knowledge of participation is what provides part of the value of Unconstrained College Students Dataset.

Ethics

Please direct any questions about the ethics of the dataset to the University of Colorado Colorado Springs Ethics and Compliance Office
For further technical information about the dataset, visit the UCCS dataset project page.

Downloads

Download EXIF data for UCCS photos: uccs_camera_exif.csv

+ +

Cite Our Work

+ + If you use our data, research, or graphics please cite our work: + +

+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}

+ +

References

a
Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.
+
a b
Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.
+

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html new file mode 100644 index 00000000..75d73824 --- /dev/null +++ b/site/public/datasets/vgg_face2/index.html @@ -0,0 +1,142 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Brainwash Dataset

+ +

+ Datasets + About +

+ +

VGG Face 2

[ page under development ]

Who used Brainwash Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research notes

The VGG Face 2 dataset includes approximately 1,331 actresses, 139 presidents, 16 wives, 3 husbands, 2 snooker player, and 1 guru
The original VGGF2 name list has been updated with the results returned from Google Knowledge
Names with a similarity score greater than 0.75 where automatically updated. Scores computed using import difflib; seq = difflib.SequenceMatcher(a=a.lower(), b=b.lower()); score = seq.ratio()
The 97 names with a score of 0.75 or lower were manually reviewed and includes name changes validating using Wikipedia.org results for names such as "Bruce Jenner" to "Caitlyn Jenner", spousal last-name changes, and discretionary changes to improve search results such as combining nicknames with full name when appropriate, for example changing "Aleksandar Petrović" to "Aleksandar 'Aco' Petrović" and minor changes such as "Mohammad Ali" to "Muhammad Ali"
The 'Description' text was automatically added when the Knowledge Graph score was greater than 250

TODO

create name list, and populate with Knowledge graph information like LFW
make list of interesting number stats, by the numbers
make list of interesting important facts
write intro abstract
write analysis of usage
find examples, citations, and screenshots of useage
find list of companies using it for table
create montages of the dataset, like LFW
create right to removal information

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/viper/index.html b/site/public/datasets/viper/index.html new file mode 100644 index 00000000..5b3ac35b --- /dev/null +++ b/site/public/datasets/viper/index.html @@ -0,0 +1,122 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

VIPeR

+ +

+ Datasets + About +

+ +

VIPeR is a person re-identification dataset of images captured at UC Santa Cruz in 2007

VIPeR contains 1,264 images and 632 persons on the UC Santa Cruz campus and is used to train person re-identification algorithms for surveillance +

VIPeR Dataset

[ page under development ]

VIPeR (Viewpoint Invariant Pedestrian Recognition) is a dataset of pedestrian images captured at University of California Santa Cruz in 2007. Accoriding to the reserachers 2 "cameras were placed in different locations in an academic setting and subjects were notified of the presence of cameras, but were not coached or instructed in any way."

VIPeR is amongst the most widely used publicly available person re-identification datasets. In 2017 the VIPeR dataset was combined into a larger person re-identification created by the Chinese University of Hong Kong called PETA (PEdesTrian Attribute).

Who used VIPeR?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how VIPeR has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Viewpoint Invariant Pedestrian Recognition was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/youtube_celebrities/index.html b/site/public/datasets/youtube_celebrities/index.html new file mode 100644 index 00000000..39670c19 --- /dev/null +++ b/site/public/datasets/youtube_celebrities/index.html @@ -0,0 +1,113 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

YouTube Celebrities

+ +

+ Datasets + About +

+ +

YouTube Celebrities

[ page under development ]

Who used YouTube Celebrities?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how YouTube Celebrities has been used around the world by commercial, military, and academic organizations; existing publicly available research citing YouTube Celebrities was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

Notes...

Selected dataset sequences: (a) MBGC, (b) CMU MoBo, (c) First +Honda/UCSD, and (d) YouTube Celebrities.
This research is supported by the Central Intelligence Agency, the Biometrics +Task Force and the Technical Support Working Group through US Army contract +W91CRB-08-C-0093. The opinions, (cid:12)ndings, and conclusions or recommendations +expressed in this publication are those of the authors and do not necessarily re(cid:13)ect +the views of our sponsors.
in "Face Recognition From Video Draft 17"
International Journal of Pattern Recognition and Artifcial Intelligence WorldScientific Publishing Company

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/index.html b/site/public/index.html new file mode 100644 index 00000000..118814be --- /dev/null +++ b/site/public/index.html @@ -0,0 +1,39 @@ + + + + MegaPixels + + + + + + + + + + +

+ +

MegaPixels

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+ DATASETS + ABOUT +

+ + + + \ No newline at end of file diff --git a/site/public/info/index.html b/site/public/info/index.html new file mode 100644 index 00000000..749c29ba --- /dev/null +++ b/site/public/info/index.html @@ -0,0 +1,50 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

+ + +

+ Datasets + About +

+ +

Face Analysis

Results are only stored for the duration of the analysis and are deleted when you leave this page.

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html new file mode 100644 index 00000000..353e3270 --- /dev/null +++ b/site/public/research/00_introduction/index.html @@ -0,0 +1,101 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

+ + +

+ Datasets + About +

+ +

00: Introduction

Posted

2018-12-15

Megapixels

+ +

Posted

Dec. 15

Author

Adam Harvey

Facial recognition is a scam.

During the last 20 years commericial, academic, and governmental agencies have promoted the false dream of a future with face recognition. This essay debunks the popular myth that such a thing ever existed.

There is no such thing as face recognition. For the last 20 years, government agencies, commercial organizations, and academic institutions have played the public as a fool, selling a roadmap of the future that simply does not exist. Facial recognition, as it is currently defined, promoted, and sold to the public, government, and commercial sector is a scam.

Committed to developing robust solutions with superhuman accuracy, the industry has repeatedly undermined itself by never actually developing anything close to "face recognition".

There is only biased feature vector clustering and probabilistic thresholding.

Motivation

Ever since government agencies began developing face recognition in the early 1960's, datasets of face images have always been central to developing and validating face recognition technologies. Today, these datasets no longer originate in labs, but instead from family photo albums posted on photo sharing sites, surveillance camera footage from college campuses, search engine queries for celebrities, cafe livestreams, or videos on YouTube.

During the last year, hundreds of these facial analysis datasets created "in the wild" have been collected to understand how they contribute to a global supply chain of biometric data that is powering the global facial recognition industry.

While many of these datasets include public figures such as politicians, athletes, and actors; they also include many non-public figures: digital activists, students, pedestrians, and semi-private shared photo albums are all considered "in the wild" and fair game for research projects. Some images are used with creative commons licenses, yet others were taken in unconstrained scenarios without awareness or consent. At first glance it appears many of the datasets were created for seemingly harmless academic research, but when examined further it becomes clear that they're also used by foreign defense agencies.

The MegaPixels site is based on an earlier installation (also supported by Mozilla) at the Tactical Tech Glassroom in London in 2017; and a commission from the Elevate arts festival curated by Berit Gilma about pedestrian recognition datasets in 2018, and research during CV Dazzle from 2010-2015. Through the many prototypes, conversations, pitches, PDFs, and false starts this project has endured during the last 5 years, it eventually evolved into something much different than originally imagined. Now, as datasets become increasingly influential in shaping the computational future, it's clear that they must be critically analyzed to understand the biases, shortcomings, funding sources, and contributions to the surveillance industry. However, it's misguided to only criticize these datasets for their flaws without also praising their contribution to society. Without publicly available facial analysis datasets there would be less public discourse, less open-source software, and less peer-reviewed research. Public datasets can indeed become a vital public good for the information economy but as this projects aims to illustrate, many ethical questions arise about consent, intellectual property, surveillance, and privacy.

+ + + + + +

Ever since the first computational facial recognition research project by the CIA in the early 1960s, data has always played a vital role in the development of our biometric future. Without facial recognition datasets there would be no facial recognition. Datasets are an indispensable part of any artificial intelligence system because, as Geoffrey Hinton points out:

Our relationship to computers has changed. Instead of programming them, we now show them and they figure it out. - Geoffrey Hinton
+

Algorithms learn from datasets. And we program algorithms by building datasets. But datasets aren't like code. There's no programming language made of data except for the data itself.

Ignore content below these lines

It was the early 2000s. Face recognition was new and no one seemed sure exactly how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier, a strategic military advantage, a way to make cities safer and to secure borders. This was the future John Ashcroft demanded with the Total Information Awareness act of the 2003 and that spooks had dreamed of for decades. It was a future that academics at Carnegie Mellon Universtiy and Colorado State University would help build. It was also a future that celebrities would play a significant role in building. And to the surprise of ordinary Internet users like myself and perhaps you, it was a future that millions of Internet users would unwittingly play role in creating.

Now the future has arrived and it doesn't make sense. Facial recognition works yet it doesn't actually work. Facial recognition is cheap and accessible but also expensive and out of control. Facial recognition research has achieved headline grabbing superhuman accuracies over 99.9% yet facial recognition is also dangerously inaccurate. During a trial installation at Sudkreuz station in Berlin in 2018, 20% of the matches were wrong, a number so low that it should not have any connection to law enforcement or justice. And in London, the Metropolitan police had been using facial recognition software that mistakenly identified an alarming 98% of people as criminals ¹, which perhaps is a crime itself.

MegaPixels is an online art project that explores the history of facial recognition from the perspective of datasets. To paraphrase the artist Trevor Paglen, whoever controls the dataset controls the meaning. MegaPixels aims to unravel the meanings behind the data and expose the darker corners of the biometric industry that have contributed to its growth. MegaPixels does not start with a conclusion, a moralistic slant, or a

Whether or not to build facial recognition was a question that can no longer be asked. As an outspoken critic of face recognition I've developed, and hopefully furthered, my understanding during the last 10 years I've spent working with computer vision. Though I initially disagreed, I've come to see technocratic perspective as a non-negotiable reality. As Oren (nytimes article) wrote in NYT Op-Ed "the horse is out of the barn" and the only thing we can do collectively or individually is to steer towards the least worse outcome. Computational communication has entered a new era and it's both exciting and frightening to explore the potentials and opportunities. In 1997 getting access to 1 teraFLOPS of computational power would have cost you $55 million and required a strategic partnership with the Department of Defense. At the time of writing, anyone can rent 1 teraFLOPS on a cloud GPU marketplace for less than $1/day. ².

I hope that this project will illuminate the darker areas of strange world of facial recognition that have not yet received attention and encourage discourse in academic, industry, and . By no means do I believe discourse can save the day. Nor do I think creating artwork can. In fact, I'm not exactly sure what the outcome of this project will be. The project is not so much what I publish here but what happens after. This entire project is only a prologue.

As McLuhan wrote, "You can't have a static, fixed position in the electric age". And in our hyper-connected age of mass surveillance, artificial intelligece, and unevenly distributed virtual futures the most irrational thing to be is rational. Increasingly the world is becoming a contradiction where people use surveillance to protest surveillance, use

Like many projects, MegaPixels had spent years meandering between formats, unfeasible budgets, and was generally too niche of a subject. The basic idea for this project, as proposed to the original Glass Room installation in 2016 in NYC, was to build an interactive mirror that showed people if they had been included in the LFW facial recognition dataset. The idea was based on my reaction to all the datasets I'd come across during research for the CV Dazzle project. I'd noticed strange datasets created for training and testing face detection algorithms. Most were created in labratory settings and their interpretation of face data was very strict.

for other post

It was the early 2000s. Face recognition was new and no one seemed sure how well it was going to perform in practice. In theory, face recognition was poised to be a game changer, a force multiplier, a strategic military advantage, a way to make cities safer and to secure the borders. It was the future that John Ashcroft demanded with the Total Information Awareness act of the 2003. It was a future that academics helped build. It was a future that celebrities helped build. And it was a future that

A decade earlier the Department of Homeland Security and the Counterdrug Technology Development Program Office initated a feasibilty study called FERET (FacE REcognition Technology) to "develop automatic face recognition capabilities that could be employed to assist security, intelligence, and law enforcement personnel in the performance of their duties [^feret_website]."

One problem with FERET dataset was that the photos were in controlled settings. For face recognition to work it would have to be used in uncontrolled settings. Even newer datasets such as the Multi-PIE (Pose, Illumination, and Expression) from Carnegie Mellon University included only indoor photos of cooperative subjects. Not only were the photos completely unrealistic, CMU's Multi-Pie included only 18 individuals and cost $500 for academic use [^cmu_multipie_cost], took years to create, and required consent from every participant.

Add progressive gan of FERET

Sharman, Jon. "Metropolitan Police's facial recognition technology 98% inaccurate, figures show". 2018. https://www.independent.co.uk/news/uk/home-news/met-police-facial-recognition-success-south-wales-trial-home-office-false-positive-a8345036.html ↩
Calle, Dan. "Supercomptuers". 1997. http://ei.cs.vt.edu/~history/SUPERCOM.Calle.HTML ↩

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html new file mode 100644 index 00000000..9426ef0f --- /dev/null +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -0,0 +1,139 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

+ + +

+ Datasets + About +

+ +

From 1 to 100 Pixels

Posted

2018-12-04

Adam Harvey

+ +

High resolution insights from low resolution data

This post will be about the meaning of "face". How do people define it? How to biometrics researchers define it? How has it changed during the last decade.

What can you know from a very small amount of information?

1 pixel grayscale
2x2 pixels grayscale, font example, can encode letters
3x3 pixels: can create a font
4x4 pixels: how many variations
8x8 yotta yotta, many more variations
5x7 face recognition
12x16 activity recognition
6/5 (up to 124/106) pixels in height/width, and the average is 24/20 for QMUL SurvFace
(prepare a Progan render of the QMUL dataset and TinyFaces)
20x16 tiny faces paper
20x20 MNIST handwritten images http://yann.lecun.com/exdb/mnist/
24x24 haarcascade detector idealized images
32x32 CIFAR image dataset
40x40 can do emotion detection, face recognition at scale, 3d modeling of the face. include datasets with faces at this resolution including pedestrian.
NIST standards begin to appear from 40x40, distinguish occular pixels
need more material from 60-100
60x60 show how texture emerges and pupils, eye color, higher resolution of features and compare to lower resolution faces
100x100 all you need for medical diagnosis
100x100 0.5% of one Instagram photo

Ideas:

Find specific cases of facial resolution being used in legal cases, forensic investigations, or military footage
resolution of boston bomber face
resolution of the state of the union image

Research

NIST report on sres states several resolutions
"Results show that the tested face recognition systems yielded similar performance for query sets with eye-to-eye distance from 60 pixels to 30 pixels" ¹

"Note that we only keep the images with a minimal side length of 80 pixels." and "a face will be labeled as “Ignore” if it is very difficult to be detected due to blurring, severe deformation and unrecognizable eyes, or the side length of its bounding box is less than 32 pixels." Ge_Detecting_Masked_Faces_CVPR_2017_paper.pdf
IBM DiF: "Faces with region size less than 50x50 or inter-ocular distance of less than 30 pixels were discarded. Faces with non-frontal pose, or anything beyond being slightly tilted to the left or the right, were also discarded."

As the resolution +formatted as rectangular databases of 16 bit RGB-tuples or 8 bit grayscale values

To consider how visual privacy applies to real world surveillance situations, the first

A single 8-bit grayscale pixel with 256 values is enough to represent the entire alphabet a-Z0-9 with room to spare.

A 2x2 pixels contains

Using no more than a 42 pixel (6x7 image) face image researchers [cite] were able to correctly distinguish between a group of 50 people. Yet

The likely outcome of face recognition research is that more data is needed to improve. Indeed, resolution is the determining factor for all biometric systems, both as training data to increase

Pixels, typically considered the buiding blocks of images and vidoes, can also be plotted as a graph of sensor values corresponding to the intensity of RGB-calibrated sensors.

Wi-Fi and cameras presents elevated risks for transmitting videos and image documentation from conflict zones, high-risk situations, or even sharing on social media. How can new developments in computer vision also be used in reverse, as a counter-forensic tool, to minimize an individual's privacy risk?

As the global Internet becomes increasingly effecient at turning the Internet into a giant dataset for machine learning, forensics, and data analysing, it would be prudent to also consider tools for decreasing the resolution. The Visual Defense module is just that. What are new ways to minimize the adverse effects of surveillance by dulling the blade. For example, a researcher paper showed that by decreasing a face size to 12x16 it was possible to do 98% accuracy with 50 people. This is clearly an example of

This research module, tentatively called Visual Defense Tools, aims to explore the

Prior Research

MPI visual privacy advisor
NIST: super resolution
YouTube blur tool
WITNESS: blur tool
Pixellated text
CV Dazzle
Bellingcat guide to geolocation
Peng! magic passport

Notes

In China, out of the approximately 200 million surveillance cameras only about 15% have enough resolution for face recognition.
In Apple's FaceID security guide, the probability of someone else's face unlocking your phone is 1 out of 1,000,000.
In England, the Metropolitan Police reported a false-positive match rate of 98% when attempting to use face recognition to locate wanted criminals.
In a face recognition trial at Berlin's Sudkreuz station, the false-match rate was 20%.

What all 3 examples illustrate is that face recognition is anything but absolute. In a 2017 talk, Jason Matheny the former directory of IARPA, admitted the face recognition is so brittle it can be subverted by using a magic marker and drawing "a few dots on your forehead". In fact face recognition is a misleading term. Face recognition is search engine for faces that can only ever show you the mos likely match. This presents real a real threat to privacy and lends

Globally, iPhone users unwittingly agree to 1/1,000,000 probably +relying on FaceID and TouchID to protect their information agree to a

NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips↩

+ +

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What Computers Can See

Posted

2018-12-15

Adam Harvey

+ +

A list of 100 things computer vision can see, eg:

age, race, gender, ancestral origin, body mass index
eye color, hair color, facial hair, glasses
beauty score,
intelligence
what you're looking at
medical conditions
tired, drowsiness in car
affectiva: interest in product, intent to buy

From PubFig Dataset

Male
Asian
White
Black
Baby
Child
Youth
Middle Aged
Senior
Black Hair
Blond Hair
Brown Hair
Bald
No Eyewear
Eyeglasses
Sunglasses
Mustache
Smiling Frowning
Chubby
Blurry
Harsh Lighting
Flash
Soft Lighting
Outdoor Curly Hair
Wavy Hair
Straight Hair
Receding Hairline
Bangs
Sideburns
Fully Visible Forehead
Partially Visible Forehead
Obstructed Forehead
Bushy Eyebrows
Arched Eyebrows
Narrow Eyes
Eyes Open
Big Nose
Pointy Nose
Big Lips
Mouth Closed
Mouth Slightly Open
Mouth Wide Open
Teeth Not Visible
No Beard
Goatee
Round Jaw
Double Chin
Wearing Hat
Oval Face
Square Face
Round Face
Color Photo
Posed Photo
Attractive Man
Attractive Woman
Indian
Gray Hair
Bags Under Eyes
Heavy Makeup
Rosy Cheeks
Shiny Skin
Pale Skin
5 o' Clock Shadow
Strong Nose-Mouth Lines
Wearing Lipstick
Flushed Face
High Cheekbones
Brown Eyes
Wearing Earrings
Wearing Necktie
Wearing Necklace

for i in {1..9};do wget http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_0$i.MP4;done;for i in {10..20}; do wget http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_$i.MP4;done

From Market 1501

The 27 attributes are:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

attribute	representation in file	label
gender	gender	male(1), female(2)
hair length	hair	short hair(1), long hair(2)
sleeve length	up	long sleeve(1), short sleeve(2)
length of lower-body clothing	down	long lower body clothing(1), short(2)
type of lower-body clothing	clothes	dress(1), pants(2)
wearing hat	hat	no(1), yes(2)
carrying backpack	backpack	no(1), yes(2)
carrying bag	bag	no(1), yes(2)
carrying handbag	handbag	no(1), yes(2)
age	age	young(1), teenager(2), adult(3), old(4)
8 color of upper-body clothing	upblack, upwhite, upred, uppurple, upyellow, upgray, upblue, upgreen	no(1), yes(2)
9 color of lower-body clothing	downblack, downwhite, downpink, downpurple, downyellow, downgray, downblue, downgreen,downbrown	no(1), yes(2)

source: https://github.com/vana77/Market-1501_Attribute/blob/master/README.md

From DukeMTMC

The 23 attributes are:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

attribute	representation in file	label
gender	gender	male(1), female(2)
length of upper-body clothing	top	short upper body clothing(1), long(2)
wearing boots	boots	no(1), yes(2)
wearing hat	hat	no(1), yes(2)
carrying backpack	backpack	no(1), yes(2)
carrying bag	bag	no(1), yes(2)
carrying handbag	handbag	no(1), yes(2)
color of shoes	shoes	dark(1), light(2)
8 color of upper-body clothing	upblack, upwhite, upred, uppurple, upgray, upblue, upgreen, upbrown	no(1), yes(2)
7 color of lower-body clothing	downblack, downwhite, downred, downgray, downblue, downgreen, downbrown	no(1), yes(2)

source: https://github.com/vana77/DukeMTMC-attribute/blob/master/README.md

From H3D Dataset

The joints and other keypoints (eyes, ears, nose, shoulders, elbows, wrists, hips, knees and ankles) +The 3D pose inferred from the keypoints. +Visibility boolean for each keypoint +Region annotations (upper clothes, lower clothes, dress, socks, shoes, hands, gloves, neck, face, hair, hat, sunglasses, bag, occluder) +Body type (male, female or child)

source: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/

From Leeds Sports Pose

=INDEX(A2:A9,MATCH(datasets!D1,B2:B9,0)) +=VLOOKUP(A2, datasets!A:J, 7, FALSE)

Right ankle +Right knee +Right hip +Left hip +Left knee +Left ankle +Right wrist +Right elbow +Right shoulder +Left shoulder +Left elbow +Left wrist +Neck +Head top

source: http://web.archive.org/web/20170915023005/sam.johnson.io/research/lsp.html

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Modal image 1

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+ + + + + \ No newline at end of file -- cgit v1.2.3-70-g09d2 From 92cdcf6e684a2de2846865e99281d939cc3ecd5a Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Tue, 16 Apr 2019 17:26:54 +0200 Subject: fix the sidebar --- .../pages/datasets/50_people_one_question/index.md | 4 +- site/content/pages/datasets/afad/index.md | 4 +- site/content/pages/datasets/brainwash/index.md | 4 +- site/content/pages/datasets/caltech_10k/index.md | 4 +- site/content/pages/datasets/celeba/index.md | 4 +- site/content/pages/datasets/cofw/index.md | 4 +- site/content/pages/datasets/duke_mtmc/index.md | 4 +- site/content/pages/datasets/feret/index.md | 4 +- .../pages/datasets/hrt_transgender/index.md | 4 +- site/content/pages/datasets/lfpw/index.md | 4 +- site/content/pages/datasets/lfw/index.md | 4 +- site/content/pages/datasets/market_1501/index.md | 4 +- site/content/pages/datasets/msceleb/index.md | 4 +- .../pages/datasets/oxford_town_centre/index.md | 4 +- site/content/pages/datasets/pipa/index.md | 4 +- site/content/pages/datasets/pubfig/index.md | 4 +- site/content/pages/datasets/vgg_face2/index.md | 3 +- site/content/pages/datasets/viper/index.md | 4 +- .../pages/datasets/youtube_celebrities/index.md | 3 +- .../datasets/50_people_one_question/index.html | 6 +-- site/public/datasets/afad/index.html | 6 +-- site/public/datasets/brainwash/index.html | 6 +-- site/public/datasets/caltech_10k/index.html | 6 +-- site/public/datasets/celeba/index.html | 6 +-- site/public/datasets/cofw/index.html | 6 +-- site/public/datasets/duke_mtmc/index.html | 15 ++++-- site/public/datasets/feret/index.html | 62 +++++++++++++++++++--- site/public/datasets/hrt_transgender/index.html | 6 +-- site/public/datasets/index.html | 24 +++++++++ site/public/datasets/lfpw/index.html | 6 +-- site/public/datasets/lfw/index.html | 6 +-- site/public/datasets/market_1501/index.html | 6 +-- site/public/datasets/msceleb/index.html | 6 +-- site/public/datasets/oxford_town_centre/index.html | 6 +-- site/public/datasets/pipa/index.html | 6 +-- site/public/datasets/pubfig/index.html | 6 +-- site/public/datasets/uccs/index.html | 4 +- site/public/datasets/vgg_face2/index.html | 6 +-- site/public/datasets/viper/index.html | 6 +-- .../public/datasets/youtube_celebrities/index.html | 4 +- todo.md | 1 - 41 files changed, 178 insertions(+), 102 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/site/content/pages/datasets/50_people_one_question/index.md b/site/content/pages/datasets/50_people_one_question/index.md index 09406f43..8b7fb931 100644 --- a/site/content/pages/datasets/50_people_one_question/index.md +++ b/site/content/pages/datasets/50_people_one_question/index.md @@ -13,11 +13,11 @@ authors: Adam Harvey ------------ +## 50 People 1 Question + ### sidebar ### end sidebar -## 50 People 1 Question - [ page under development ] {% include 'dashboard.html' %} \ No newline at end of file diff --git a/site/content/pages/datasets/afad/index.md b/site/content/pages/datasets/afad/index.md index c941cbc6..755506d8 100644 --- a/site/content/pages/datasets/afad/index.md +++ b/site/content/pages/datasets/afad/index.md @@ -10,11 +10,11 @@ authors: Adam Harvey ------------ +## Asian Face Age Dataset + ### sidebar ### end sidebar -## Asian Face Age Dataset - [ page under development ] {% include 'dashboard.html' %} diff --git a/site/content/pages/datasets/brainwash/index.md b/site/content/pages/datasets/brainwash/index.md index 6bb7f287..156b02c7 100644 --- a/site/content/pages/datasets/brainwash/index.md +++ b/site/content/pages/datasets/brainwash/index.md @@ -14,11 +14,11 @@ authors: Adam Harvey ------------ +## Brainwash Dataset + ### sidebar ### end sidebar -## Brainwash Dataset - *Brainwash* is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe"[^readme] captured at 100 second intervals throught the entire day. Brainwash dataset was captured during 3 days in 2014: October 27, November 13, and November 24. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com.[^end_to_end] Brainwash is not a widely used dataset but since its publication by Stanford University in 2015, it has notably appeared in several research papers from the National University of Defense Technology in Changsha, China. In 2016 and in 2017 researchers there conducted studies on detecting people's heads in crowded scenes for the purpose of surveillance. [^localized_region_context] [^replacement_algorithm] diff --git a/site/content/pages/datasets/caltech_10k/index.md b/site/content/pages/datasets/caltech_10k/index.md index b69dbe43..db2383c7 100644 --- a/site/content/pages/datasets/caltech_10k/index.md +++ b/site/content/pages/datasets/caltech_10k/index.md @@ -11,11 +11,11 @@ authors: Adam Harvey ------------ +## Caltech 10K Faces Dataset + ### sidebar ### end sidebar -## Caltech 10K Faces Dataset - [ page under development ] {% include 'dashboard.html' %} diff --git a/site/content/pages/datasets/celeba/index.md b/site/content/pages/datasets/celeba/index.md index 9cdbcad9..3f3aea79 100644 --- a/site/content/pages/datasets/celeba/index.md +++ b/site/content/pages/datasets/celeba/index.md @@ -13,11 +13,11 @@ authors: Adam Harvey ------------ +## CelebA Dataset + ### sidebar ### end sidebar -## CelebA Dataset - [ PAGE UNDER DEVELOPMENT ] {% include 'dashboard.html' %} diff --git a/site/content/pages/datasets/cofw/index.md b/site/content/pages/datasets/cofw/index.md index 4c9f1d61..3cafe5b1 100644 --- a/site/content/pages/datasets/cofw/index.md +++ b/site/content/pages/datasets/cofw/index.md @@ -11,11 +11,11 @@ authors: Adam Harvey ------------ +## Caltech Occluded Faces in the Wild + ### sidebar ### end sidebar -## Caltech Occluded Faces in the Wild - [ PAGE UNDER DEVELOPMENT ] {% include 'dashboard.html' %} diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index 37b2cd2d..ac0a3f2e 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -13,11 +13,11 @@ authors: Adam Harvey ------------ +## Duke MTMC + ### sidebar ### end sidebar -## Duke MTMC - [ page under development ] Duke MTMC (Multi-Target, Multi-Camera Tracking) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking algorithms are used for citywide dragnet surveillance systems such as those used throughout China by SenseTime[^sensetime_qz] and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets[^sensenets_uyghurs]. In fact researchers from both SenseTime[^sensetime1] [^sensetime2] and SenseNets[^sensenets_sensetime] used the Duke MTMC dataset for their research. diff --git a/site/content/pages/datasets/feret/index.md b/site/content/pages/datasets/feret/index.md index 15e5ccf0..034ff4aa 100644 --- a/site/content/pages/datasets/feret/index.md +++ b/site/content/pages/datasets/feret/index.md @@ -11,11 +11,11 @@ authors: Adam Harvey ------------ +# FacE REcognition Dataset (FERET) + ### sidebar ### end sidebar -# FacE REcognition Dataset (FERET) - [ page under development ] {% include 'dashboard.html' %} diff --git a/site/content/pages/datasets/hrt_transgender/index.md b/site/content/pages/datasets/hrt_transgender/index.md index 1bca6a33..137e6dcb 100644 --- a/site/content/pages/datasets/hrt_transgender/index.md +++ b/site/content/pages/datasets/hrt_transgender/index.md @@ -14,11 +14,11 @@ authors: Adam Harvey ------------ +## HRT Transgender Dataset + ### sidebar ### end sidebar -## HRT Transgender Dataset - [ page under development ] {% include 'dashboard.html' } \ No newline at end of file diff --git a/site/content/pages/datasets/lfpw/index.md b/site/content/pages/datasets/lfpw/index.md index 48c0d82a..09506313 100644 --- a/site/content/pages/datasets/lfpw/index.md +++ b/site/content/pages/datasets/lfpw/index.md @@ -11,11 +11,11 @@ authors: Adam Harvey ------------ +## Labeled Face Parts in The Wild + ### sidebar ### end sidebar -## Labeled Face Parts in The Wild - {% include 'dashboard.html' %} -------- diff --git a/site/content/pages/datasets/lfw/index.md b/site/content/pages/datasets/lfw/index.md index bc7f3222..5d90e87f 100644 --- a/site/content/pages/datasets/lfw/index.md +++ b/site/content/pages/datasets/lfw/index.md @@ -13,11 +13,11 @@ authors: Adam Harvey ------------ +## Labeled Faces in the Wild + ### sidebar ### end sidebar -## Labeled Faces in the Wild - [ PAGE UNDER DEVELOPMENT ] *Labeled Faces in The Wild* (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition[^lfw_www]. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com[^lfw_pingan], LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong." diff --git a/site/content/pages/datasets/market_1501/index.md b/site/content/pages/datasets/market_1501/index.md index 67558bef..e106a498 100644 --- a/site/content/pages/datasets/market_1501/index.md +++ b/site/content/pages/datasets/market_1501/index.md @@ -13,11 +13,11 @@ authors: Adam Harvey ------------ +## Market-1501 Dataset + ### sidebar ### end sidebar -## Market-1501 Dataset - [ PAGE UNDER DEVELOPMENT] {% include 'dashboard.html' %} diff --git a/site/content/pages/datasets/msceleb/index.md b/site/content/pages/datasets/msceleb/index.md index 6d45a0aa..70e85699 100644 --- a/site/content/pages/datasets/msceleb/index.md +++ b/site/content/pages/datasets/msceleb/index.md @@ -14,11 +14,11 @@ authors: Adam Harvey ------------ +## Microsoft Celeb Dataset (MS Celeb) + ### sidebar ### end sidebar -## Microsoft Celeb Dataset (MS Celeb) - [ PAGE UNDER DEVELOPMENT ] https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology diff --git a/site/content/pages/datasets/oxford_town_centre/index.md b/site/content/pages/datasets/oxford_town_centre/index.md index c26b81e7..c32cd022 100644 --- a/site/content/pages/datasets/oxford_town_centre/index.md +++ b/site/content/pages/datasets/oxford_town_centre/index.md @@ -14,11 +14,11 @@ authors: Adam Harvey ------------ +## Oxford Town Centre + ### sidebar ### end sidebar -## Oxford Town Centre - The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems.[^ben_benfold_orig] The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009[^guiding_surveillance] the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more. The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating they neither knew of or consented to participation in the research project. diff --git a/site/content/pages/datasets/pipa/index.md b/site/content/pages/datasets/pipa/index.md index fbf535a9..250878ff 100644 --- a/site/content/pages/datasets/pipa/index.md +++ b/site/content/pages/datasets/pipa/index.md @@ -13,11 +13,11 @@ authors: Adam Harvey ------------ +## People in Photo Albums + ### sidebar ### end sidebar -## People in Photo Albums - [ PAGE UNDER DEVELOPMENT ] {% include 'dashboard.html' %} diff --git a/site/content/pages/datasets/pubfig/index.md b/site/content/pages/datasets/pubfig/index.md index 560e66de..5f2e1ad5 100644 --- a/site/content/pages/datasets/pubfig/index.md +++ b/site/content/pages/datasets/pubfig/index.md @@ -13,11 +13,11 @@ authors: Adam Harvey ------------ +## PubFig + ### sidebar ### end sidebar -## PubFig - [ PAGE UNDER DEVELOPMENT ] {% include 'dashboard.html' %} diff --git a/site/content/pages/datasets/vgg_face2/index.md b/site/content/pages/datasets/vgg_face2/index.md index d18d1c2e..acf2476e 100644 --- a/site/content/pages/datasets/vgg_face2/index.md +++ b/site/content/pages/datasets/vgg_face2/index.md @@ -10,12 +10,11 @@ authors: Adam Harvey ------------ +## VGG Face 2 ### sidebar ### end sidebar -## VGG Face 2 - [ page under development ] {% include 'dashboard.html' %} diff --git a/site/content/pages/datasets/viper/index.md b/site/content/pages/datasets/viper/index.md index e6dd167a..291b2136 100644 --- a/site/content/pages/datasets/viper/index.md +++ b/site/content/pages/datasets/viper/index.md @@ -14,11 +14,11 @@ authors: Adam Harvey ------------ +## VIPeR Dataset + ### sidebar ### end sidebar -## VIPeR Dataset - [ page under development ] *VIPeR (Viewpoint Invariant Pedestrian Recognition)* is a dataset of pedestrian images captured at University of California Santa Cruz in 2007. Accoriding to the reserachers 2 "cameras were placed in different locations in an academic setting and subjects were notified of the presence of cameras, but were not coached or instructed in any way." diff --git a/site/content/pages/datasets/youtube_celebrities/index.md b/site/content/pages/datasets/youtube_celebrities/index.md index 6ce2a347..49bfaa2e 100644 --- a/site/content/pages/datasets/youtube_celebrities/index.md +++ b/site/content/pages/datasets/youtube_celebrities/index.md @@ -10,12 +10,11 @@ authors: Adam Harvey ------------ +## YouTube Celebrities ### sidebar ### end sidebar -## YouTube Celebrities - [ page under development ] {% include 'dashboard.html' %} diff --git a/site/public/datasets/50_people_one_question/index.html b/site/public/datasets/50_people_one_question/index.html index dfd8cbff..76d22562 100644 --- a/site/public/datasets/50_people_one_question/index.html +++ b/site/public/datasets/50_people_one_question/index.html @@ -27,7 +27,8 @@

People One Question is a dataset of people from an online video series on YouTube and Vimeo used for building facial recogntion algorithms

People One Question dataset includes ... -

50 People 1 Question

[ page under development ]

Who used 50 People One Question Dataset?

diff --git a/site/public/datasets/afad/index.html b/site/public/datasets/afad/index.html index df14e7cd..832ce86a 100644 --- a/site/public/datasets/afad/index.html +++ b/site/public/datasets/afad/index.html @@ -26,7 +26,8 @@

[ page under development ]

Who used Asian Face Age Dataset?

diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html index 03331a2d..494856ec 100644 --- a/site/public/datasets/brainwash/index.html +++ b/site/public/datasets/brainwash/index.html @@ -27,7 +27,8 @@

Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco in 2014

The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms -

Brainwash Dataset

diff --git a/site/public/datasets/caltech_10k/index.html b/site/public/datasets/caltech_10k/index.html index 00b5e7fd..c7b9f894 100644 --- a/site/public/datasets/caltech_10k/index.html +++ b/site/public/datasets/caltech_10k/index.html @@ -26,7 +26,8 @@

[ page under development ]

Who used Brainwash Dataset?

diff --git a/site/public/datasets/celeba/index.html b/site/public/datasets/celeba/index.html index c4caef20..e42ceb6f 100644 --- a/site/public/datasets/celeba/index.html +++ b/site/public/datasets/celeba/index.html @@ -27,7 +27,8 @@

CelebA is a dataset of people...

CelebA includes... -

CelebA Dataset

[ PAGE UNDER DEVELOPMENT ]

Who used CelebA Dataset?

diff --git a/site/public/datasets/cofw/index.html b/site/public/datasets/cofw/index.html index 4851e256..39e9680b 100644 --- a/site/public/datasets/cofw/index.html +++ b/site/public/datasets/cofw/index.html @@ -26,7 +26,8 @@

[ PAGE UNDER DEVELOPMENT ]

Who used COFW Dataset?

diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index ba32484a..78067101 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -27,7 +27,8 @@

Duke MTMC is a dataset of surveillance camera footage of students on Duke University campus

Duke MTMC contains over 2 million video frames and 2,700 unique identities collected from 8 HD cameras at Duke University campus in March 2014 -

Duke MTMC

[ page under development ]

In this investigation into the Duke MTMC dataset, we found that researchers at Duke University in Durham, North Carolina captured over 2,000 students, faculty members, and passersby into one of the most prolific public surveillance research datasets that's used around the world by commercial and defense surveillance organizations.

Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.

Who used Duke MTMC Dataset?

@@ -217,7 +218,11 @@ under Grants IIS-10-17017 and IIS-14-20894.

booktitle = {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking}, year = {2016} } -

References

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
+
ToDo
+
- clean up citations, formatting
+

References

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
a
https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/
a b
"Attention-Aware Compositional Network for Person Re-identification". 2018. SemanticScholar, PDF
a b
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. SemanticScholar, PDF
diff --git a/site/public/datasets/feret/index.html b/site/public/datasets/feret/index.html index 089cd351..929041df 100644 --- a/site/public/datasets/feret/index.html +++ b/site/public/datasets/feret/index.html @@ -26,7 +26,8 @@
-
+
FacE REcognition Dataset (FERET)
+

Published

2007

@@ -41,10 +42,59 @@

Website

umass.edu
-
FacE REcognition Dataset (FERET)
-
[ page under development ]
-
{% include 'dashboard.html' %}
-
(ignore) RESEARCH below this line
+
[ page under development ]
+
+
Who used LFW?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how LFW has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Labeled Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
(ignore) RESEARCH below this line

Years: 1993-1996

Images: 14,126
@@ -63,7 +113,7 @@

"A release form is necessary because of the privacy laws in the United States."

-
Funding
+
Funding

The FERET program is sponsored by the U.S. Depart- ment of Defense’s Counterdrug Technology Development Program Office. The U.S. Army Research Laboratory (ARL) is the technical agent for the FERET program. ARL designed, administered, and scored the FERET tests. George Mason University collected, processed, and main- tained the FERET database. Inquiries regarding the FERET database or test should be directed to P. Jonathon Phillips.
diff --git a/site/public/datasets/hrt_transgender/index.html b/site/public/datasets/hrt_transgender/index.html index 231a5271..5f2229d8 100644 --- a/site/public/datasets/hrt_transgender/index.html +++ b/site/public/datasets/hrt_transgender/index.html @@ -27,7 +27,8 @@

TBD
TBD -
+
HRT Transgender Dataset
+

Published

2013

@@ -42,8 +43,7 @@

Website

faceaginggroup.com
-
HRT Transgender Dataset
-
[ page under development ]
+
[ page under development ]

{% include 'dashboard.html' }

diff --git a/site/public/datasets/index.html b/site/public/datasets/index.html index b01c1ac1..75961089 100644 --- a/site/public/datasets/index.html +++ b/site/public/datasets/index.html @@ -61,6 +61,30 @@

+ +

+ HRT Transgender Dataset +

2013

gender transition and facial recognition

10,564 images

+ + + +

+ Microsoft Celeb +

2016

Large-scale face recognition

1,000,000 images

100,000

+ +

Oxford Town Centre diff --git a/site/public/datasets/lfpw/index.html b/site/public/datasets/lfpw/index.html index f734d332..c26b8583 100644 --- a/site/public/datasets/lfpw/index.html +++ b/site/public/datasets/lfpw/index.html @@ -26,7 +26,8 @@

Who used LFWP?

diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 60a6bf0e..1907f959 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -27,7 +27,8 @@

Labeled Faces in The Wild (LFW) is the first facial recognition dataset created entirely from online photos

It includes 13,456 images of 4,432 people's images copied from the Internet during 2002-2004 and is the most frequently used dataset in the world for benchmarking face recognition algorithms. -

Labeled Faces in the Wild

[ PAGE UNDER DEVELOPMENT ]

Labeled Faces in The Wild (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition 1. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com 3, LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong."

The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. LFW is a subset of Names of Faces and is part of the first facial recognition training dataset created entirely from images appearing on the Internet. The people appearing in LFW are...

The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.

diff --git a/site/public/datasets/market_1501/index.html b/site/public/datasets/market_1501/index.html index 72807efc..ad6bf458 100644 --- a/site/public/datasets/market_1501/index.html +++ b/site/public/datasets/market_1501/index.html @@ -27,7 +27,8 @@

Market-1501 is a dataset is collection of CCTV footage from Tsinghua University

The Market-1501 dataset includes 1,261 people from 5 HD surveillance cameras located on campus -

Market-1501 Dataset

[ PAGE UNDER DEVELOPMENT]

Who used Market 1501?

diff --git a/site/public/datasets/msceleb/index.html b/site/public/datasets/msceleb/index.html index be21280c..b4d02c87 100644 --- a/site/public/datasets/msceleb/index.html +++ b/site/public/datasets/msceleb/index.html @@ -27,7 +27,8 @@

MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms

The MS Celeb dataset includes over 10,000,000 images and 93,000 identities of semi-public figures collected using the Bing search engine -

Microsoft Celeb Dataset (MS Celeb)

[ PAGE UNDER DEVELOPMENT ]

https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology

https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm

diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html index af020855..8c95f287 100644 --- a/site/public/datasets/oxford_town_centre/index.html +++ b/site/public/datasets/oxford_town_centre/index.html @@ -27,7 +27,8 @@

Oxford Town Centre is a dataset of surveillance camera footage from Cornmarket St Oxford, England

The Oxford Town Centre dataset includes approximately 2,200 identities and is used for research and development of face recognition systems -

Oxford Town Centre

The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating they neither knew of or consented to participation in the research project.

Who used TownCentre?

diff --git a/site/public/datasets/pipa/index.html b/site/public/datasets/pipa/index.html index 780b3029..d02540f0 100644 --- a/site/public/datasets/pipa/index.html +++ b/site/public/datasets/pipa/index.html @@ -27,7 +27,8 @@

People in Photo Albums (PIPA) is a dataset...

[ add subdescrition ] -

People in Photo Albums

[ PAGE UNDER DEVELOPMENT ]

Who used PIPA Dataset?

diff --git a/site/public/datasets/pubfig/index.html b/site/public/datasets/pubfig/index.html index 2c8bd7b1..ed593054 100644 --- a/site/public/datasets/pubfig/index.html +++ b/site/public/datasets/pubfig/index.html @@ -27,7 +27,8 @@

PubFig is a dataset...

[ add subdescrition ] -

PubFig

[ PAGE UNDER DEVELOPMENT ]

Who used PubFig?

diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html index 1d76de3a..27d30716 100644 --- a/site/public/datasets/uccs/index.html +++ b/site/public/datasets/uccs/index.html @@ -28,7 +28,7 @@

UnConstrained College Students is a dataset of long-range surveillance photos of students on University of Colorado in Colorado Springs campus

The UnConstrained College Students dataset includes 16,149 images of 1,732 students, faculty, and pedestrians and is used for developing face recognition and face detection algorithms

UnConstrained College Students

According to the author's of the the UnConstrained College Students dataset it is primarliy used for research and development of "face detection and recognition research towards surveillance applications that are becoming more popular and more required nowadays, and where no automatic recognition algorithm has proven to be useful yet." Applications of this technology include usage by defense and intelligence agencies, who were also the primary funding sources of the UCCS dataset.

In the two papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), the researchers disclosed their funding sources as ODNI (United States Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. Further, UCCS's VAST site explicity states they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests.

Location on campus where students were unknowingly photographed with a telephoto lens to be used for defense and intelligence agency funded research on face recognition. Image: Google Maps

The UCCS dataset includes the highest resolution images of any publicly available face recognition dataset discovered so far (18MP) and was, as of 2018, the "largest surveillance FR benchmark in the public domain." 3 To create the dataset, the researchers used a Canon 7D digital camera fitted with a Sigma 800mm telephoto lens and photographed students from a distance of 150–200m through their office window. Photos were taken during the morning and afternoon while students were walking to and from classes. According to an analysis of the EXIF data embedded in the photos, nearly half of the 16,149 photos were taken on Tuesdays. The most popular time was during lunch break. All of the photos were taken during the spring semester in 2012 and 2013 but the dataset was not publicy released until 2016.

diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html index 75d73824..3c2859a5 100644 --- a/site/public/datasets/vgg_face2/index.html +++ b/site/public/datasets/vgg_face2/index.html @@ -26,7 +26,8 @@

[ page under development ]

Who used Brainwash Dataset?

diff --git a/site/public/datasets/viper/index.html b/site/public/datasets/viper/index.html index 5b3ac35b..494c249b 100644 --- a/site/public/datasets/viper/index.html +++ b/site/public/datasets/viper/index.html @@ -27,7 +27,8 @@

VIPeR is a person re-identification dataset of images captured at UC Santa Cruz in 2007

VIPeR contains 1,264 images and 632 persons on the UC Santa Cruz campus and is used to train person re-identification algorithms for surveillance -

VIPeR Dataset

[ page under development ]

diff --git a/site/public/datasets/youtube_celebrities/index.html b/site/public/datasets/youtube_celebrities/index.html index 39670c19..9a6ae18e 100644 --- a/site/public/datasets/youtube_celebrities/index.html +++ b/site/public/datasets/youtube_celebrities/index.html @@ -26,8 +26,8 @@

YouTube Celebrities

[ page under development ]

YouTube Celebrities

[ page under development ]

Who used YouTube Celebrities?

diff --git a/todo.md b/todo.md index cc4736cd..4586611e 100644 --- a/todo.md +++ b/todo.md @@ -16,7 +16,6 @@ ## Datasets - JL: this paper isn't appearing in the UCCS list of verified papers but should be included https://arxiv.org/pdf/1708.02337.pdf -- JL: add h2 dataset title above the right-sidebar so title extends full width - AH: add dataset analysis for MS Celeb, IJB-C - AH: fix dataset analysis for UCCS, brainwahs graphics - AH: add license information to each dataset page -- cgit v1.2.3-70-g09d2 From 776ae57da4a27966d58aa76bcac1eed67b75687b Mon Sep 17 00:00:00 2001 From: adamhrv Date: Tue, 16 Apr 2019 17:28:49 +0200 Subject: add right-sidebar, add lsat_updated --- megapixels/app/site/parser.py | 2 +- site/assets/css/css.css | 21 ++++++++---- .../pages/datasets/uccs/assets/uccs_grid.jpg | Bin 0 -> 142280 bytes site/content/pages/datasets/uccs/index.md | 36 ++++++++++++--------- site/public/datasets/duke_mtmc/index.html | 11 +++++-- site/public/datasets/index.html | 24 ++++++++++++++ site/public/datasets/uccs/index.html | 36 ++++++++++++--------- 7 files changed, 88 insertions(+), 42 deletions(-) create mode 100644 site/content/pages/datasets/uccs/assets/uccs_grid.jpg (limited to 'site/public/datasets/duke_mtmc') diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py index 6b71e041..80ba9ece 100644 --- a/megapixels/app/site/parser.py +++ b/megapixels/app/site/parser.py @@ -62,7 +62,7 @@ def parse_markdown(metadata, sections, s3_path, skip_h1=False): elif in_stats and not section.strip().startswith('## ') and 'end sidebar' not in section.lower(): current_group.append(section) elif in_stats and section.strip().startswith('## ') or 'end sidebar' in section.lower(): - current_group = [format_section(current_group, s3_path, 'left-sidebar', tag='div')] + current_group = [format_section(current_group, s3_path, 'right-sidebar', tag='div')] if 'end sidebar' not in section.lower(): current_group.append(section) in_stats = False diff --git a/site/assets/css/css.css b/site/assets/css/css.css index 1c7b8859..81a38487 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -265,10 +265,10 @@ section { } section p { margin: 10px auto 20px auto; - line-height: 2; + line-height: 1.85rem; font-size: 17px; - font-weight: 300; - color: #dedede; + font-weight: 400; + color: #cdcdcd; } section ul { margin: 10px auto 20px auto; @@ -288,12 +288,13 @@ p.subp{ .content a { color: #fff; text-decoration: none; - border-bottom: 1px dashed; + border-bottom: 2px solid #666; + padding-bottom: 1px; transition: color 0.1s cubic-bezier(0,0,1,1); } .desktop .content a:hover { color: #fff; - border-bottom: 1px solid; + border-bottom: 2px solid #ccc; } /* top of post metadata */ @@ -357,6 +358,12 @@ p.subp{ text-decoration: none; border-bottom: 1px solid; } +.left-sidebar a, .right-sidebar a{ + border-bottom: 1px solid #666; +} +.content .left-sidebar a:hover, .content .right-sidebar a:hover{ + border-bottom: 1px solid #ccc; +} /* lists */ @@ -733,7 +740,7 @@ section.fullwidth .image { } .dataset-list .dataset { width: 300px; - height: 180px; + height: 178px; padding: 10px; color: white; font-weight: 400; @@ -762,6 +769,8 @@ section.fullwidth .image { } .dataset-list .title { margin-bottom: 10px; + padding: 2px 4px; + } .dataset-list .fields { display: block; diff --git a/site/content/pages/datasets/uccs/assets/uccs_grid.jpg b/site/content/pages/datasets/uccs/assets/uccs_grid.jpg new file mode 100644 index 00000000..d3d898ea Binary files /dev/null and b/site/content/pages/datasets/uccs/assets/uccs_grid.jpg differ diff --git a/site/content/pages/datasets/uccs/index.md b/site/content/pages/datasets/uccs/index.md index 767f8220..1ad63f18 100644 --- a/site/content/pages/datasets/uccs/index.md +++ b/site/content/pages/datasets/uccs/index.md @@ -20,38 +20,41 @@ authors: Adam Harvey ## UnConstrained College Students -UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge" [^funding_uccs]. In this investigation, we examine the funding sources, contents of the dataset, photo EXIF data, and publicy available research project citations. +UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications"[^uccs_vast]. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge".[^funding_uccs] In this investigation, we examine the contents of the dataset, funding sources, photo EXIF data, and information from publicly available research project citations. -According to the author's of the the UnConstrained College Students dataset it is primarliy used for research and development of "face detection and recognition research towards surveillance applications that are becoming more popular and more required nowadays, and where no automatic recognition algorithm has proven to be useful yet." Applications of this technology include usage by defense and intelligence agencies, who were also the primary funding sources of the UCCS dataset. +The UCCS dataset includes over 1,700 unique identities, most of which are students walking to and from class. As of 2018, it was the "largest surveillance [face recognition] benchmark in the public domain."[^surv_face_qmul] The photos were taken during the spring semesters of 2012 – 2013 on the West Lawn of the University of Colorado Colorado Springs campus. The photographs were timed to capture students during breaks between their scheduled classes in the morning and afternoon during Monday through Thursday. "For example, a student taking Monday-Wednesday classes at 12:30 PM will show up in the camera on almost every Monday and Wednesday."[^sapkota_boult]. +![caption: Example images from the UnConstrained College Students Dataset. ](assets/uccs_grid.jpg) +The long-range surveillance images in the UnContsrained College Students dataset were captured using a Canon 7D 18 megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured."[^sapkota_boult] +Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face detection and recognition systems for real world applications in defense, intelligence, and commercial applications. -In the two papers associated with the release of the UCCS datasaet ([Unconstrained Face Detection and Open-Set Face Recognition Challenge](https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745) and [Large Scale Unconstrained Open Set Face Database](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1)), the researchers disclosed their funding sources as ODNI (United States Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. Further, UCCS's VAST site explicity [states](https://vast.uccs.edu/project/iarpa-janus/) they are part of the [IARPA Janus](https://www.iarpa.gov/index.php/research-programs/janus), a face recognition project developed to serve the needs of national intelligence interests. +![caption: The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps](assets/uccs_map_aerial.jpg) -![caption: Location on campus where students were unknowingly photographed with a telephoto lens to be used for defense and intelligence agency funded research on face recognition. Image: Google Maps](assets/uccs_map_aerial.jpg) +The EXIF data embedded in the images shows that the photo capture times follow a similar pattern, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken on Friday shows that the researchers were only interested in capturing images of students. -The UCCS dataset includes the highest resolution images of any publicly available face recognition dataset discovered so far (18MP) and was, as of 2018, the "largest surveillance FR benchmark in the public domain."[^surv_face_qmul] To create the dataset, the researchers used a Canon 7D digital camera fitted with a Sigma 800mm telephoto lens and photographed students from a distance of 150–200m through their office window. Photos were taken during the morning and afternoon while students were walking to and from classes. According to an analysis of the EXIF data embedded in the photos, nearly half of the 16,149 photos were taken on Tuesdays. The most popular time was during lunch break. All of the photos were taken during the spring semester in 2012 and 2013 but the dataset was not publicy released until 2016. +![caption: UCCS photos captured per weekday © megapixels.cc](assets/uccs_exif_plot_days.png) -In 2017 the UCCS face dataset was used for a defense and intelligence agency funded [face recognition challenge](http://www.face-recognition-challenge.com/) at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the [2nd Unconstrained Face Detection and Open Set Recognition Challenge](https://erodner.github.io/ial2018eccv/) at the European Computer Vision Conference (ECCV) in Munich, Germany. Additional research projects that have used the UCCS dataset are included below in the list of verified citations. +![caption: UCCS photos captured per weekday © megapixels.cc](assets/uccs_exif_plot.png) -As of April 15, 2019, the UCCS dataset is no longer available for public download. During the three years it was publicly available (2016-2019) the UCCS dataset apepared in at least 5 publicly available research papers including verified usage from University of Notre Dame (US), Beihang University (China), Beckman Institute (US), Queen Mary University of London (UK), Carnegie Mellon University (US),Karlsruhe Institute of Technology (DE), and Vision Semantics Ltd (UK) who [lists](http://visionsemantics.com/partners.html) the UK Ministry of Defence and Metropolitan Police as partners. +The two research papers associated with the release of the UCCS dataset ([Unconstrained Face Detection and Open-Set Face Recognition Challenge](https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745) and [Large Scale Unconstrained Open Set Face Database](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1)), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), Small Business Innovation Research (SBIR), Special Operations Command and Small Business Innovation Research (SOCOM SBIR), and the National Science Foundation. Further, UCCS's VAST site explicitly [states](https://vast.uccs.edu/project/iarpa-janus/) they are part of the [IARPA Janus](https://www.iarpa.gov/index.php/research-programs/janus), a face recognition project developed to serve the needs of national intelligence interests, clearly establishing the the funding sources and immediate benefactors of this dataset are United States defense and intelligence agencies. + + +Although the images were first captured in 2012 – 2013 the dataset was not publicly released until 2016. Then in 2017 the UCCS face dataset formed the basis for a defense and intelligence agency funded [face recognition challenge](http://www.face-recognition-challenge.com/) project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the [2nd Unconstrained Face Detection and Open Set Recognition Challenge](https://erodner.github.io/ial2018eccv/) at the European Computer Vision Conference (ECCV) in Munich, Germany. + +As of April 15, 2019, the UCCS dataset is no longer available for public download. But during the three years it was publicly available (2016-2019) the UCCS dataset appeared in at least 6 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military. -To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions. -![caption: GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018](assets/uccs_pgan_01.jpg) {% include 'dashboard.html' %} {% include 'supplementary_header.html' %} -### Dates and Times - -The images in UCCS were taken on 18 non-consecutive days during 2012–2013. Analysis of the [EXIF data](assets/uccs_camera_exif.csv) embedded in original images reveal that most of the images were taken on Tuesdays, and the most frequent capture time throughout the week was 12:30PM. -![caption: UCCS photos captured per weekday © megapixels.cc](assets/uccs_exif_plot_days.png) +To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions. -![caption: UCCS photos captured per 10-minute intervals per weekday © megapixels.cc](assets/uccs_exif_plot.png) +![caption: GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018](assets/uccs_pgan_01.jpg) === columns 2 @@ -114,7 +117,7 @@ If you attended University of Colorado Colorado Springs and were captured by the ### Ethics - Please direct any questions about the ethics of the dataset to the University of Colorado Colorado Springs [Ethics and Compliance Office](https://www.uccs.edu/compliance/) -- For further technical information about the dataset, visit the [UCCS dataset project page](https://vast.uccs.edu/Opensetface). +- For further technical information about the UnConstrained College Students dataset, visit the [UCCS dataset project page](https://vast.uccs.edu/Opensetface). ### Downloads @@ -126,6 +129,7 @@ If you attended University of Colorado Colorado Springs and were captured by the ### Footnotes -[^funding_sb]: Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013. +[^uccs_vast]: "2nd Unconstrained Face Detection and Open Set Recognition Challenge." . Accessed April 15, 2019. +[^sapkota_boult]: Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013. [^funding_uccs]: Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3. [^surv_face_qmul]: "Surveillance Face Recognition Challenge". [SemanticScholar](https://www.semanticscholar.org/paper/Surveillance-Face-Recognition-Challenge-Cheng-Zhu/2306b2a8fba28539306052764a77a0d0f5d1236a) diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index ba32484a..c64c0934 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -46,11 +46,12 @@

Website

duke.edu

Duke MTMC

[ page under development ]

Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.

Who used Duke MTMC Dataset?

@@ -217,7 +218,11 @@ under Grants IIS-10-17017 and IIS-14-20894.

booktitle = {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking}, year = {2016} } -

References

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
+
ToDo
+
- clean up citations, formatting
+

References

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
a
https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/
a b
"Attention-Aware Compositional Network for Person Re-identification". 2018. SemanticScholar, PDF
a b
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. SemanticScholar, PDF
diff --git a/site/public/datasets/index.html b/site/public/datasets/index.html index b01c1ac1..75961089 100644 --- a/site/public/datasets/index.html +++ b/site/public/datasets/index.html @@ -61,6 +61,30 @@ + +
+ HRT Transgender Dataset +
+
2013
+
gender transition and facial recognition
+
10,564 images
+
38
+
+
+ + + +
+ Microsoft Celeb +
+
2016
+
Large-scale face recognition
+
1,000,000 images
+
100,000
+
+
+ +
Oxford Town Centre diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html index 4a0dfb5e..42774635 100644 --- a/site/public/datasets/uccs/index.html +++ b/site/public/datasets/uccs/index.html @@ -4,7 +4,7 @@ MegaPixels - + @@ -26,7 +26,7 @@
-
UnConstrained College Students is a dataset of long-range surveillance photos of students at University of Colorado in Colorado Springs
The UnConstrained College Students dataset includes 16,149 images and 1,732 identities of subjects on University of Colorado Colorado Springs campus and is used for making face recognition and face detection algorithms +
UnConstrained College Students is a dataset of long-range surveillance photos of students on University of Colorado in Colorado Springs campus
The UnConstrained College Students dataset includes 16,149 images of 1,732 students, faculty, and pedestrians and is used for developing face recognition and face detection algorithms

Published

2016
@@ -49,13 +49,14 @@
Website

uccs.edu

UnConstrained College Students
-
[ page under development ]
-
UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs. According to the authors of two papers associated with the dataset, subjects were "photographed using a long-range high-resolution surveillance camera without their knowledge" 2. To create the dataset, the researchers used a Canon 7D digital camera fitted with a Sigma 800mm telephoto lens and photographed students 150–200m away through their office window. Photos were taken during the morning and afternoon while students were walking to and from classes. The primary uses of this dataset are to train, validate, and build recognition and face detection algorithms for realistic surveillance scenarios.
-
What makes the UCCS dataset unique is that it includes the highest resolution images of any publicly available face recognition dataset discovered so far (18MP), that it was captured on a campus without consent or awareness using a long-range telephoto lens, and that it was funded by United States defense and intelligence agencies.
-
Combined funding sources for the creation of the initial and final release of the dataset include ODNI (Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. 1 2
-
In 2017 the UCCS face dataset was used for a defense and intelligence agency funded face recognition challenge at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was used for the 2nd Unconstrained Face Detection and Open Set Recognition Challenge at the European Computer Vision Conference (ECCV) in Munich, Germany. Additional research projects that have used the UCCS dataset are included below in the list of verified citations.
-
UCCS is part of the IARAP Janus team https://vast.uccs.edu/project/iarpa-janus/
-
https://arxiv.org/abs/1708.02337
+
UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications" 1. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge". 3 In this investigation, we examine the contents of the dataset, funding sources, photo EXIF data, and information from publicly available research project citations.
+
The UCCS dataset includes over 1,700 unique identities, most of which are students walking to and from class. As of 2018, it was the "largest surveillance [face recognition] benchmark in the public domain." 4 The photos were taken during the spring semesters of 2012 – 2013 on the West Lawn of the University of Colorado Colorado Springs campus. The photographs were timed to capture students during breaks between their scheduled classes in the morning and afternoon during Monday through Thursday. "For example, a student taking Monday-Wednesday classes at 12:30 PM will show up in the camera on almost every Monday and Wednesday." 2.
+
Example images from the UnConstrained College Students Dataset.
The long-range surveillance images in the UnContsrained College Students dataset were captured using a Canon 7D 18 megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured." 2 +Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face detection and recognition systems for real world applications in defense, intelligence, and commercial applications.
+
The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps
The EXIF data embedded in the images shows that the photo capture times follow a similar pattern, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken on Friday shows that the researchers were only interested in capturing images of students.
+
UCCS photos captured per weekday © megapixels.cc
UCCS photos captured per weekday © megapixels.cc
The two research papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), Small Business Innovation Research (SBIR), Special Operations Command and Small Business Innovation Research (SOCOM SBIR), and the National Science Foundation. Further, UCCS's VAST site explicitly states they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests, clearly establishing the the funding sources and immediate benefactors of this dataset are United States defense and intelligence agencies.
+
Although the images were first captured in 2012 – 2013 the dataset was not publicly released until 2016. Then in 2017 the UCCS face dataset formed the basis for a defense and intelligence agency funded face recognition challenge project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the 2nd Unconstrained Face Detection and Open Set Recognition Challenge at the European Computer Vision Conference (ECCV) in Munich, Germany.
+
As of April 15, 2019, the UCCS dataset is no longer available for public download. But during the three years it was publicly available (2016-2019) the UCCS dataset appeared in at least 6 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military.

Who used UCCS?
@@ -116,9 +117,8 @@
Supplementary Information
-
Dates and Times
-
The images in UCCS were taken on 18 non-consecutive days during 2012–2013. Analysis of the EXIF data embedded in original images reveal that most of the images were taken on Tuesdays, and the most frequent capture time throughout the week was 12:30PM.
-
UCCS photos captured per weekday © megapixels.cc
UCCS photos captured per 10-minute intervals per weekday © megapixels.cc
UCCS photos taken in 2012
+
To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions.
+
GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018
UCCS photos taken in 2012
@@ -212,7 +212,7 @@

Date

Location

The location of the camera and subjects can confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The original papers also provides another clue: a picture of the camera inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the location on Google Maps
-
Location on campus where students were unknowingly photographed with a telephoto lens to be used for defense and intelligence agency funded research on face recognition. Image: Google Maps
3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps
Funding
+
3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps
Funding

The UnConstrained College Students dataset is associated with two main research papers: "Large Scale Unconstrained Open Set Face Database" and "Unconstrained Face Detection and Open-Set Face Recognition Challenge". Collectively, these papers and the creation of the dataset have received funding from the following organizations:

ONR (Office of Naval Research) MURI (The Department of Defense Multidisciplinary University Research Initiative) grant N00014-08-1-0638
@@ -227,7 +227,7 @@
Ethics

Please direct any questions about the ethics of the dataset to the University of Colorado Colorado Springs Ethics and Compliance Office
-
For further technical information about the dataset, visit the UCCS dataset project page.
+
For further technical information about the UnConstrained College Students dataset, visit the UCCS dataset project page.

Downloads

@@ -250,8 +250,12 @@ }
-
References
a
Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.
-
a b
Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.
+
+
This page was last updated on 2019-4-15
+
References
a
"2nd Unconstrained Face Detection and Open Set Recognition Challenge." https://vast.uccs.edu/Opensetface/. Accessed April 15, 2019.
+
a b
Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.
+
a
Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.
+
a
"Surveillance Face Recognition Challenge". SemanticScholar

-- cgit v1.2.3-70-g09d2 From 9d6d12f0b16d10219c62f25ce036b9377417de70 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Tue, 16 Apr 2019 18:55:02 +0200 Subject: build --- megapixels/app/site/parser.py | 3 +- .../datasets/50_people_one_question/index.html | 56 +++++++++- site/public/datasets/afad/index.html | 56 +++++++++- site/public/datasets/brainwash/index.html | 86 +++++++++++++-- site/public/datasets/caltech_10k/index.html | 58 +++++++++- site/public/datasets/celeba/index.html | 58 +++++++++- site/public/datasets/cofw/index.html | 117 +++++++++++++++++++-- site/public/datasets/duke_mtmc/index.html | 99 ++++++++++++++--- site/public/datasets/feret/index.html | 58 +++++++++- site/public/datasets/hrt_transgender/index.html | 6 +- site/public/datasets/lfpw/index.html | 56 +++++++++- site/public/datasets/lfw/index.html | 77 ++++++++++++-- site/public/datasets/market_1501/index.html | 56 +++++++++- site/public/datasets/msceleb/index.html | 68 +++++++++++- site/public/datasets/oxford_town_centre/index.html | 89 ++++++++++++++-- site/public/datasets/pipa/index.html | 56 +++++++++- site/public/datasets/pubfig/index.html | 56 +++++++++- site/public/datasets/uccs/index.html | 103 +++++++++++++++--- site/public/datasets/vgg_face2/index.html | 58 +++++++++- site/public/datasets/viper/index.html | 56 +++++++++- .../public/datasets/youtube_celebrities/index.html | 56 +++++++++- 21 files changed, 1212 insertions(+), 116 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py index aa2ddcda..1489d056 100644 --- a/megapixels/app/site/parser.py +++ b/megapixels/app/site/parser.py @@ -60,8 +60,7 @@ def parse_markdown(metadata, sections, s3_path, skip_h1=False): current_group.append(section) in_stats = True if 'end sidebar' in section.lower(): - groups.append(format_section(current_group, s3_path, 'right-sidebar', tag='div')) - current_group = [] + current_group = [format_section(current_group, s3_path, 'right-sidebar', tag='div')] in_stats = False elif in_stats and not section.strip().startswith('## ') and 'end sidebar' not in section.lower(): current_group.append(section) diff --git a/site/public/datasets/50_people_one_question/index.html b/site/public/datasets/50_people_one_question/index.html index 577d4d8c..dc7919f7 100644 --- a/site/public/datasets/50_people_one_question/index.html +++ b/site/public/datasets/50_people_one_question/index.html @@ -28,7 +28,7 @@
People One Question is a dataset of people from an online video series on YouTube and Vimeo used for building facial recogntion algorithms
People One Question dataset includes ...
50 People 1 Question
-
+

Published

2013

@@ -40,8 +40,58 @@

Website

caltech.edu
-
[ page under development ]
-
{% include 'dashboard.html' %}
+
[ page under development ]
+
+
Who used 50 People One Question Dataset?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how 50 People One Question Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing 50 People One Question was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +

diff --git a/site/public/datasets/afad/index.html b/site/public/datasets/afad/index.html index 6ef13948..f2b0a5ba 100644 --- a/site/public/datasets/afad/index.html +++ b/site/public/datasets/afad/index.html @@ -42,9 +42,59 @@

Website

github.io
-
[ page under development ]
-
{% include 'dashboard.html' %}
-
(ignore) research notes
+
[ page under development ]
+

Who used Asian Face Age Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research notes

The Asian Face Age Dataset (AFAD) is a new dataset proposed for evaluating the performance of age estimation, which contains more than 160K facial images and the corresponding age and gender labels. This dataset is oriented to age estimation on Asian faces, so all the facial images are for Asian faces. It is noted that the AFAD is the biggest dataset for age estimation to date. It is well suited to evaluate how deep learning methods can be adopted for age estimation. Motivation

For age estimation, there are several public datasets for evaluating the performance of a specific algorithm, such as FG-NET [1] (1002 face images), MORPH I (1690 face images), and MORPH II[2] (55,608 face images). Among them, the MORPH II is the biggest public dataset to date. On the other hand, as we know it is necessary to collect a large scale dataset to train a deep Convolutional Neural Network. Therefore, the MORPH II dataset is extensively used to evaluate how deep learning methods can be adopted for age estimation [3][4].
diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html index 2a6044d0..95f0d77d 100644 --- a/site/public/datasets/brainwash/index.html +++ b/site/public/datasets/brainwash/index.html @@ -28,7 +28,7 @@
Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco in 2014
The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms
Brainwash Dataset
-
+

Published

2015

@@ -49,12 +49,70 @@

Website

stanford.edu
-
Brainwash is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe" 1 captured at 100 second intervals throught the entire day. Brainwash dataset was captured during 3 days in 2014: October 27, November 13, and November 24. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com. 2
+
Brainwash is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe" 1 captured at 100 second intervals throught the entire day. Brainwash dataset was captured during 3 days in 2014: October 27, November 13, and November 24. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com. 2

Brainwash is not a widely used dataset but since its publication by Stanford University in 2015, it has notably appeared in several research papers from the National University of Defense Technology in Changsha, China. In 2016 and in 2017 researchers there conducted studies on detecting people's heads in crowded scenes for the purpose of surveillance. 3 4

If you happen to have been at Brainwash cafe in San Francisco at any time on October 26, November 13, or November 24 in 2014 you are most likely included in the Brainwash dataset and have unwittingly contributed to surveillance research.
-
{% include 'dashboard.html' %}
-
{% include 'supplementary_header.html' %}
-
A visualization of 81,973 head annotations from the Brainwash dataset training partition. © megapixels.cc
An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains about 12,000 images. License: Open Data Commons Public Domain Dedication (PDDL)
49 of the 11,918 images included in the Brainwash dataset. License: Open Data Commons Public Domain Dedication (PDDL)
TODO
+
+
Who used Brainwash Dataset?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how Brainwash Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Brainwash Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
+ +
+
+
+
+ +
Supplementary Information
+ +
A visualization of 81,973 head annotations from the Brainwash dataset training partition. © megapixels.cc
An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains about 12,000 images. License: Open Data Commons Public Domain Dedication (PDDL)
49 of the 11,918 images included in the Brainwash dataset. License: Open Data Commons Public Domain Dedication (PDDL)
TODO

change supp images to 2x2 grid with bboxes

add bounding boxes to the header image
@@ -62,7 +120,23 @@
add ethics link to Stanford

add optout info

-
{% include 'cite_our_work.html' %}
+
+ +
Cite Our Work
+
+ + If you use our data, research, or graphics please cite our work: + +
+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}
+ +
References
a
"readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.

a
Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016.

a
Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.
diff --git a/site/public/datasets/caltech_10k/index.html b/site/public/datasets/caltech_10k/index.html index 4cbb7ce6..04d63ee3 100644 --- a/site/public/datasets/caltech_10k/index.html +++ b/site/public/datasets/caltech_10k/index.html @@ -27,7 +27,7 @@

Caltech 10K Faces Dataset
-
+

Published

2015

@@ -48,9 +48,59 @@

Website

stanford.edu
-
[ page under development ]
-
{% include 'dashboard.html' %}
-
(ignore) research notes
+
[ page under development ]
+
+
Who used Brainwash Dataset?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how Brainwash Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Brainwash Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
(ignore) research notes

The dataset contains images of people collected from the web by typing common given names into Google Image Search. The coordinates of the eyes, the nose and the center of the mouth for each frontal face are provided in a ground truth file. This information can be used to align and crop the human faces or as a ground truth for a face detection algorithm. The dataset has 10,524 human faces of various resolutions and in different settings, e.g. portrait images, groups of people, etc. Profile faces or very low resolution faces are not labeled.

diff --git a/site/public/datasets/celeba/index.html b/site/public/datasets/celeba/index.html index 9d75b428..c72f3798 100644 --- a/site/public/datasets/celeba/index.html +++ b/site/public/datasets/celeba/index.html @@ -28,7 +28,7 @@
CelebA is a dataset of people...
CelebA includes...
CelebA Dataset
-
+

Published

2015

@@ -46,9 +46,59 @@

Website

edu.hk
-
[ PAGE UNDER DEVELOPMENT ]
-
{% include 'dashboard.html' %}
-
Research
+
[ PAGE UNDER DEVELOPMENT ]
+
+
Who used CelebA Dataset?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how CelebA Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Large-scale CelebFaces Attributes Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
Research

"An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks" mentions use by sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their"

7dab6fbf42f82f0f5730fc902f72c3fb628ef2f0
diff --git a/site/public/datasets/cofw/index.html b/site/public/datasets/cofw/index.html index 084cf7c2..eef8cf5e 100644 --- a/site/public/datasets/cofw/index.html +++ b/site/public/datasets/cofw/index.html @@ -27,7 +27,7 @@

Caltech Occluded Faces in the Wild
-
+

Published

2013

@@ -39,10 +39,60 @@

Website

caltech.edu
-
[ PAGE UNDER DEVELOPMENT ]
-
{% include 'dashboard.html' %}
-
(ignore) research notes
-
Years
1993-1996
Images
14,126
Identities
1,199
Origin
Web Searches
Funded by
ODNI, IARPA, Microsoft
COFW is "is designed to benchmark face landmark algorithms in realistic conditions, which include heavy occlusions and large shape variations" [Robust face landmark estimation under occlusion].
+
[ PAGE UNDER DEVELOPMENT ]
+
+
Who used COFW Dataset?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how COFW Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Caltech Occluded Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
(ignore) research notes
+
Years
1993-1996
Images
14,126
Identities
1,199
Origin
Web Searches
Funded by
ODNI, IARPA, Microsoft
COFW is "is designed to benchmark face landmark algorithms in realistic conditions, which include heavy occlusions and large shape variations" [Robust face landmark estimation under occlusion].

We asked four people with different levels of computer vision knowledge to each collect 250 faces representative of typical real-world images, with the clear goal of challenging computer vision methods. The result is 1,007 images of faces obtained from a variety of sources.

@@ -54,11 +104,58 @@ To increase the number of training images, and since COFW has the exact same la
This research is supported by NSF Grant 0954083 and by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2014-14071600012.

https://www.cs.cmu.edu/~peiyunh/topdown/
-
{% include 'map.html' %}
-
{% include 'supplementary_header.html' %}
-
{% include 'citations.html' %}
-
{% include 'chart.html' %}
-
TODO
+
+ +
Biometric Trade Routes
+ +
+ To help understand how COFW Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Caltech Occluded Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the location markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org and then dataset usage verified and geolocated.
+
+ +
+
+
+
+ +
Supplementary Information
+ +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
+
Who used COFW Dataset?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
TODO

- replace graphic

diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 3cd19708..5cb6fb0c 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -28,7 +28,7 @@
Duke MTMC is a dataset of surveillance camera footage of students on Duke University campus
Duke MTMC contains over 2 million video frames and 2,700 unique identities collected from 8 HD cameras at Duke University campus in March 2014
Duke MTMC
-
+

Published

2016

@@ -46,21 +46,78 @@

Website

duke.edu
-
[ page under development ]
+
[ page under development ]

Duke MTMC (Multi-Target, Multi-Camera Tracking) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking algorithms are used for citywide dragnet surveillance systems such as those used throughout China by SenseTime 1 and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets 2. In fact researchers from both SenseTime 4 5 and SenseNets 3 used the Duke MTMC dataset for their research.

In this investigation into the Duke MTMC dataset, we found that researchers at Duke University in Durham, North Carolina captured over 2,000 students, faculty members, and passersby into one of the most prolific public surveillance research datasets that's used around the world by commercial and defense surveillance organizations.

Since it's publication in 2016, the Duke MTMC dataset has been used in over 100 studies at organizations around the world including SenseTime 4 5, SenseNets 3, IARPA and IBM 9, Chinese National University of Defense 7 8, US Department of Homeland Security 10, Tencent, Microsoft, Microsft Asia, Fraunhofer, Senstar Corp., Alibaba, Naver Labs, Google and Hewlett-Packard Labs to name only a few.

The creation and publication of the Duke MTMC dataset in 2014 (published in 2016) was originally funded by the U.S. Army Research Laboratory and the National Science Foundation 6. Though our analysis of the geographic locations of the publicly available research shows over twice as many citations by researchers from China (44% China, 20% United States). In 2018 alone, there were 70 research project citations from China.
-
A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research and development dataset on . These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. Open Data Commons Attribution License.
The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy". 6. Camera 5 was positioned to capture students as entering and exiting the university's main chapel. Each camera's location and approximate field of view. The heat map visualization shows the locations where pedestrians were most frequently annotated in each video from the Duke MTMC dataset.
-
Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.
Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc
{% include 'dashboard.html' %}
-
{% include 'supplementary_header.html' %}
-
Funding
+
A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research and development dataset on . These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. Open Data Commons Attribution License.
The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy". 6. Camera 5 was positioned to capture students as entering and exiting the university's main chapel. Each camera's location and approximate field of view. The heat map visualization shows the locations where pedestrians were most frequently annotated in each video from the Duke MTMC dataset.
+
Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.
Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc
+
Who used Duke MTMC Dataset?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how Duke MTMC Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Duke Multi-Target, Multi-Camera Tracking Project was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
+ +
+
+
+
+ +
Supplementary Information
+ +
Funding

Original funding for the Duke MTMC dataset was provided by the Army Research Office under Grant No. W911NF-10-1-0387 and by the National Science Foundation under Grants IIS-10-17017 and IIS-14-20894.

Video Timestamps

The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least aligns the relative time. The rainy weather on that day also contribute towards the likelihood of March 14, 2014..
-
=== columns 2
- +
@@ -95,8 +152,7 @@ under Grants IIS-10-17017 and IIS-14-20894.

Camera Date

-
===========
- +
@@ -131,15 +187,30 @@ under Grants IIS-10-17017 and IIS-14-20894.

Camera Date

-
=== end columns
-
Opting Out
+
Opting Out

If you attended Duke University and were captured by any of the 8 surveillance cameras positioned on campus in 2014, there is unfortunately no way to be removed. The dataset files have been distributed throughout the world and it would not be possible to contact all the owners for removal. Nor do the authors provide any options for students to opt-out, nor did they even inform students they would be used at test subjects for surveillance research and development in a project funded, in part, by the United States Army Research Office.

Notes

The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812

-
{% include 'cite_our_work.html' %}
-
If you use any data from the Duke MTMC please follow their license and cite their work as:
+
+ +
Cite Our Work
+
+ + If you use our data, research, or graphics please cite our work: + +
+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}
+ +
+
If you use any data from the Duke MTMC please follow their license and cite their work as:
 @inproceedings{ristani2016MTMC,
  title =        {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
diff --git a/site/public/datasets/feret/index.html b/site/public/datasets/feret/index.html
index 8af139ab..387826b0 100644
--- a/site/public/datasets/feret/index.html
+++ b/site/public/datasets/feret/index.html
@@ -42,9 +42,59 @@
   
     Website
     umass.edu
-  
[ page under development ]
-{% include 'dashboard.html' %}
-(ignore) RESEARCH below this line
+  [ page under development ]
+
+
Who used LFW?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how LFW has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Labeled Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
(ignore) RESEARCH below this line

Years: 1993-1996

Images: 14,126
@@ -63,7 +113,7 @@

"A release form is necessary because of the privacy laws in the United States."

-
Funding
+
Funding

The FERET program is sponsored by the U.S. Depart- ment of Defense’s Counterdrug Technology Development Program Office. The U.S. Army Research Laboratory (ARL) is the technical agent for the FERET program. ARL designed, administered, and scored the FERET tests. George Mason University collected, processed, and main- tained the FERET database. Inquiries regarding the FERET database or test should be directed to P. Jonathon Phillips.

diff --git a/site/public/datasets/hrt_transgender/index.html b/site/public/datasets/hrt_transgender/index.html index 15cf4807..6b9ae7be 100644 --- a/site/public/datasets/hrt_transgender/index.html +++ b/site/public/datasets/hrt_transgender/index.html @@ -28,7 +28,7 @@
TBD
TBD
HRT Transgender Dataset
-
+

Published

2013

@@ -43,8 +43,8 @@

Website

faceaginggroup.com
-
[ page under development ]
-
{% include 'dashboard.html' }
+
[ page under development ]
+
{% include 'dashboard.html' }

diff --git a/site/public/datasets/lfpw/index.html b/site/public/datasets/lfpw/index.html index 7f16cd01..45de2599 100644 --- a/site/public/datasets/lfpw/index.html +++ b/site/public/datasets/lfpw/index.html @@ -27,7 +27,7 @@

Labeled Face Parts in The Wild
-
+

Published

2011

@@ -36,8 +36,58 @@

Website

neerajkumar.org
-
{% include 'dashboard.html' %}
-
RESEARCH below this line
+
+
Who used LFWP?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how LFWP has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Labeled Face Parts in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
RESEARCH below this line

Release 1 of LFPW consists of 1,432 faces from images downloaded from the web using simple text queries on sites such as google.com, flickr.com, and yahoo.com. Each image was labeled by three MTurk workers, and 29 fiducial points, shown below, are included in dataset. LFPW was originally described in the following publication:

Due to copyright issues, we cannot distribute image files in any format to anyone. Instead, we have made available a list of image URLs where you can download the images yourself. We realize that this makes it impossible to exactly compare numbers, as image links will slowly disappear over time, but we have no other option. This seems to be the way other large web-based databases seem to be evolving.

diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 54b10611..7997629f 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -28,7 +28,7 @@
Labeled Faces in The Wild (LFW) is the first facial recognition dataset created entirely from online photos
It includes 13,456 images of 4,432 people's images copied from the Internet during 2002-2004 and is the most frequently used dataset in the world for benchmarking face recognition algorithms.
Labeled Faces in the Wild
-
+

Published

2007

@@ -43,21 +43,76 @@

Website

umass.edu
-
[ PAGE UNDER DEVELOPMENT ]
+
[ PAGE UNDER DEVELOPMENT ]

Labeled Faces in The Wild (LFW) is "a database of face photographs designed for studying the problem of unconstrained face recognition 1. It is used to evaluate and improve the performance of facial recognition algorithms in academic, commercial, and government research. According to BiometricUpdate.com 3, LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong."

The LFW dataset includes 13,233 images of 5,749 people that were collected between 2002-2004. LFW is a subset of Names of Faces and is part of the first facial recognition training dataset created entirely from images appearing on the Internet. The people appearing in LFW are...

The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.

The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.
-
All 5,379 people in the Labeled Faces in The Wild Dataset. Showing one face per person
The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.
+
All 5,379 people in the Labeled Faces in The Wild Dataset. Showing one face per person
The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.

The Names and Faces dataset was the first face recognition dataset created entire from online photos. However, Names and Faces and LFW are not the first face recognition dataset created entirely "in the wild". That title belongs to the UCD dataset. Images obtained "in the wild" means using an image without explicit consent or awareness from the subject or photographer.
-
{% include 'dashboard.html' %}
-
{% include 'supplementary_header.html' %}
-
Commercial Use
+
+
Who used LFW?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how LFW has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Labeled Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
+ +
+
+
+
+ +
Supplementary Information
+ +
Commercial Use

Add a paragraph about how usage extends far beyond academia into research centers for largest companies in the world. And even funnels into CIA funded research in the US and defense industry usage in China.
-
load_file assets/lfw_commercial_use.csv
-name_display, company_url, example_url, country, description
-
-
Research
+
Research

"In our experiments, we used 10000 images and associated captions from the Faces in the wilddata set [3]."

"This work was supported in part by the Center for Intelligent Information Retrieval, the Central Intelligence Agency, the National Security Agency and National Science Foundation under CAREER award IIS-0546666 and grant IIS-0326249."
@@ -77,7 +132,7 @@ name_display, company_url, example_url, country, description
The dataset includes 2 images of George Tenet, the former Director of Central Intelligence (DCI) for the Central Intelligence Agency whose facial biometrics were eventually used to help train facial recognition software in China and Russia

./15/155205b8e288fd49bf203135871d66de879c8c04/paper.txt shows usage by DSTO Australia, supported parimal@iisc.ac.in

-
Created
2002 – 2004
Images
13,233
Identities
5,749
Origin
Yahoo! News Images
Used by
Facebook, Google, Microsoft, Baidu, Tencent, SenseTime, Face++, CIA, NSA, IARPA
Website
umass.edu
+
Created
2002 – 2004
Images
13,233
Identities
5,749
Origin
Yahoo! News Images
Used by
Facebook, Google, Microsoft, Baidu, Tencent, SenseTime, Face++, CIA, NSA, IARPA
Website
umass.edu
There are about 3 men for every 1 woman in the LFW dataset 1

The person with the most images is George W. Bush with 530

There are about 3 George W. Bush's for every 1 Tony Blair
diff --git a/site/public/datasets/market_1501/index.html b/site/public/datasets/market_1501/index.html index a76a8859..7c545335 100644 --- a/site/public/datasets/market_1501/index.html +++ b/site/public/datasets/market_1501/index.html @@ -43,9 +43,59 @@

Website

liangzheng.org
-
[ PAGE UNDER DEVELOPMENT]
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{% include 'dashboard.html' %}
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(ignore) research Notes
+
[ PAGE UNDER DEVELOPMENT]
+
+
Who used Market 1501?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how Market 1501 has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Market 1501 Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
(ignore) research Notes
"MARS is an extension of the Market-1501 dataset. During collection, we placed six near synchronized cameras in the campus of Tsinghua university. There were Five 1,0801920 HD cameras and one 640480 SD camera. MARS consists of 1,261 different pedestrians whom are captured by at least 2 cameras. Given a query tracklet, MARS aims to retrieve tracklets that contain the same ID." - main paper
bbox "0065C1T0002F0016.jpg", "0065" is the ID of the pedestrian. "C1" denotes the first diff --git a/site/public/datasets/msceleb/index.html b/site/public/datasets/msceleb/index.html index 60b08b50..8b070118 100644 --- a/site/public/datasets/msceleb/index.html +++ b/site/public/datasets/msceleb/index.html @@ -28,7 +28,7 @@
MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms
The MS Celeb dataset includes over 10,000,000 images and 93,000 identities of semi-public figures collected using the Bing search engine
Microsoft Celeb Dataset (MS Celeb)
-
+

Published

2016

@@ -49,12 +49,70 @@

Website

msceleb.org
-
[ PAGE UNDER DEVELOPMENT ]
+
[ PAGE UNDER DEVELOPMENT ]

https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology

https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm
-
{% include 'dashboard.html' %}
-
{% include 'supplementary_header.html' %}
-
Additional Information
+
+
Who used Microsoft Celeb?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how Microsoft Celeb has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Microsoft Celebrity Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
+ +
+
+
+
+ +
Supplementary Information
+ +
Additional Information

The dataset author spoke about his research at the CVPR conference in 2016 https://www.youtube.com/watch?v=Nl2fBKxwusQ

diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html index d6f7378f..b48efe3e 100644 --- a/site/public/datasets/oxford_town_centre/index.html +++ b/site/public/datasets/oxford_town_centre/index.html @@ -28,7 +28,7 @@
Oxford Town Centre is a dataset of surveillance camera footage from Cornmarket St Oxford, England
The Oxford Town Centre dataset includes approximately 2,200 identities and is used for research and development of face recognition systems
Oxford Town Centre
-
+

Published

2009

@@ -49,18 +49,89 @@

Website

ox.ac.uk
-
The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems. 1 The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009 2 the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more.
+
The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems. 1 The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009 2 the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among dozens more.

The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating they neither knew of or consented to participation in the research project.
-
{% include 'dashboard.html' %}
-
{% include 'supplementary_header.html' %}
-
Location
+
+
Who used TownCentre?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how TownCentre has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Oxford Town Centre was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
+ +
+
+
+
+ +
Supplementary Information
+ +
Location

The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs source. At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their research projects it is likely that they would also be able to access to this camera.

To discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera pointing in the same direction as the Oxford Town Centre dataset proving the camera can and has been rotated before.

As for the capture date, the text on the storefront display shows a sale happening from December 2nd – 7th indicating the capture date was between or just before those dates. The capture year is either 2008 or 2007 since prior to 2007 the Carphone Warehouse (photo, history) did not exist at this location. Since the sweaters in the GAP window display are more similar to those in a GAP website snapshot from November 2007, our guess is that the footage was obtained during late November or early December 2007. The lack of street vendors and slight waste residue near the bench suggests that is was probably a weekday after rubbish removal.
-
Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (map)
==== columns
-
Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc
====
-
Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc
=== end columns
-
{% include 'cite_our_work.html' %}
+
Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (map)
Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc
Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc
+ +
Cite Our Work
+
+ + If you use our data, research, or graphics please cite our work: + +
+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}
+ +
References
a
Benfold, Ben and Reid, Ian. "Stable Multi-Target Tracking in Real-Time Surveillance Video". CVPR 2011. Pages 3457-3464.

a
"Guiding Visual Surveillance by Tracking Human Attention". 2009.

diff --git a/site/public/datasets/pipa/index.html b/site/public/datasets/pipa/index.html index 28da8d4b..6c920b46 100644 --- a/site/public/datasets/pipa/index.html +++ b/site/public/datasets/pipa/index.html @@ -28,7 +28,7 @@
People in Photo Albums (PIPA) is a dataset...
[ add subdescrition ]
People in Photo Albums
-
+

Published

2015

@@ -46,8 +46,58 @@

Website

berkeley.edu
-
[ PAGE UNDER DEVELOPMENT ]
-
{% include 'dashboard.html' %}
+
[ PAGE UNDER DEVELOPMENT ]
+
+
Who used PIPA Dataset?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how PIPA Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing People in Photo Albums Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +

diff --git a/site/public/datasets/pubfig/index.html b/site/public/datasets/pubfig/index.html index 1a6ffebf..e81e12bc 100644 --- a/site/public/datasets/pubfig/index.html +++ b/site/public/datasets/pubfig/index.html @@ -28,7 +28,7 @@
PubFig is a dataset...
[ add subdescrition ]
PubFig
-
+

Published

2009

@@ -43,8 +43,58 @@

Website

columbia.edu
-
[ PAGE UNDER DEVELOPMENT ]
-
{% include 'dashboard.html' %}
+
[ PAGE UNDER DEVELOPMENT ]
+
+
Who used PubFig?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how PubFig has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Public Figures Face Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +

diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html index 4c106922..32f7cdb2 100644 --- a/site/public/datasets/uccs/index.html +++ b/site/public/datasets/uccs/index.html @@ -28,7 +28,7 @@
UnConstrained College Students is a dataset of long-range surveillance photos of students on University of Colorado in Colorado Springs campus
The UnConstrained College Students dataset includes 16,149 images of 1,732 students, faculty, and pedestrians and is used for developing face recognition and face detection algorithms
UnConstrained College Students
-
+

Published

2016

@@ -49,20 +49,77 @@

Website

uccs.edu
-
UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications" 1. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge". 3 In this investigation, we examine the contents of the dataset, funding sources, photo EXIF data, and information from publicly available research project citations.
+
UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications" 1. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge". 3 In this investigation, we examine the contents of the dataset, funding sources, photo EXIF data, and information from publicly available research project citations.

The UCCS dataset includes over 1,700 unique identities, most of which are students walking to and from class. As of 2018, it was the "largest surveillance [face recognition] benchmark in the public domain." 4 The photos were taken during the spring semesters of 2012 – 2013 on the West Lawn of the University of Colorado Colorado Springs campus. The photographs were timed to capture students during breaks between their scheduled classes in the morning and afternoon during Monday through Thursday. "For example, a student taking Monday-Wednesday classes at 12:30 PM will show up in the camera on almost every Monday and Wednesday." 2.
-
Example images from the UnConstrained College Students Dataset.
The long-range surveillance images in the UnContsrained College Students dataset were captured using a Canon 7D 18 megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured." 2 +
Example images from the UnConstrained College Students Dataset.
The long-range surveillance images in the UnContsrained College Students dataset were captured using a Canon 7D 18 megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured." 2 Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face detection and recognition systems for real world applications in defense, intelligence, and commercial applications.
-
The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps
In the two papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), the researchers disclosed their funding sources as ODNI (United States Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. Further, UCCS's VAST site explicity states they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests.
+
The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps
In the two papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), the researchers disclosed their funding sources as ODNI (United States Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. Further, UCCS's VAST site explicity states they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests.

The EXIF data embedded in the images shows that the photo capture times follow a similar pattern, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken on Friday shows that the researchers were only interested in capturing images of students.
-
UCCS photos captured per weekday © megapixels.cc
UCCS photos captured per weekday © megapixels.cc
The two research papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), Small Business Innovation Research (SBIR), Special Operations Command and Small Business Innovation Research (SOCOM SBIR), and the National Science Foundation. Further, UCCS's VAST site explicitly states they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests, clearly establishing the the funding sources and immediate benefactors of this dataset are United States defense and intelligence agencies.
+
UCCS photos captured per weekday © megapixels.cc
UCCS photos captured per weekday © megapixels.cc
The two research papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), Small Business Innovation Research (SBIR), Special Operations Command and Small Business Innovation Research (SOCOM SBIR), and the National Science Foundation. Further, UCCS's VAST site explicitly states they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests, clearly establishing the the funding sources and immediate benefactors of this dataset are United States defense and intelligence agencies.

Although the images were first captured in 2012 – 2013 the dataset was not publicly released until 2016. Then in 2017 the UCCS face dataset formed the basis for a defense and intelligence agency funded face recognition challenge project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the 2nd Unconstrained Face Detection and Open Set Recognition Challenge at the European Computer Vision Conference (ECCV) in Munich, Germany.

As of April 15, 2019, the UCCS dataset is no longer available for public download. But during the three years it was publicly available (2016-2019) the UCCS dataset appeared in at least 6 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military.
-
{% include 'dashboard.html' %}
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{% include 'supplementary_header.html' %}
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To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions.
-
GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018
=== columns 2
-
UCCS photos taken in 2012
+
+
Who used UCCS?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how UCCS has been used around the world by commercial, military, and academic organizations; existing publicly available research citing UnConstrained College Students Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
+
Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
+ +
+
+
+
+ +
Supplementary Information
+ +
To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions.
+
GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018
UCCS photos taken in 2012
@@ -120,8 +177,7 @@ Their setup made it impossible for students to know they were being photographed

Date

-
===========
-
UCCS photos taken in 2013
+
UCCS photos taken in 2013
@@ -155,10 +211,9 @@ Their setup made it impossible for students to know they were being photographed

Date

-
=== end columns
-
Location
+
Location

The location of the camera and subjects can confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The original papers also provides another clue: a picture of the camera inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the location on Google Maps
-
3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps
Funding
+
3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps
Funding

The UnConstrained College Students dataset is associated with two main research papers: "Large Scale Unconstrained Open Set Face Database" and "Unconstrained Face Detection and Open-Set Face Recognition Challenge". Collectively, these papers and the creation of the dataset have received funding from the following organizations:

ONR (Office of Naval Research) MURI (The Department of Defense Multidisciplinary University Research Initiative) grant N00014-08-1-0638
@@ -179,7 +234,23 @@ Their setup made it impossible for students to know they were being photographed

Download EXIF data for UCCS photos: uccs_camera_exif.csv

-
{% include 'cite_our_work.html' %}
+
+ +
Cite Our Work
+
+ + If you use our data, research, or graphics please cite our work: + +
+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}
+ +
References
a
"2nd Unconstrained Face Detection and Open Set Recognition Challenge." https://vast.uccs.edu/Opensetface/. Accessed April 15, 2019.

a b
Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.

a
Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.
diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html index e23a3afd..a9d318f1 100644 --- a/site/public/datasets/vgg_face2/index.html +++ b/site/public/datasets/vgg_face2/index.html @@ -48,9 +48,59 @@

Website

stanford.edu
-
[ page under development ]
-
{% include 'dashboard.html' %}
-
(ignore) research notes
+
[ page under development ]
+
+
Who used Brainwash Dataset?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how Brainwash Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Brainwash Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
+
+
Academic
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Commercial
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Military / Government
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Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
(ignore) research notes

The VGG Face 2 dataset includes approximately 1,331 actresses, 139 presidents, 16 wives, 3 husbands, 2 snooker player, and 1 guru

The original VGGF2 name list has been updated with the results returned from Google Knowledge
@@ -58,7 +108,7 @@
The 97 names with a score of 0.75 or lower were manually reviewed and includes name changes validating using Wikipedia.org results for names such as "Bruce Jenner" to "Caitlyn Jenner", spousal last-name changes, and discretionary changes to improve search results such as combining nicknames with full name when appropriate, for example changing "Aleksandar Petrović" to "Aleksandar 'Aco' Petrović" and minor changes such as "Mohammad Ali" to "Muhammad Ali"

The 'Description' text was automatically added when the Knowledge Graph score was greater than 250

-
TODO
+
TODO
create name list, and populate with Knowledge graph information like LFW

make list of interesting number stats, by the numbers
diff --git a/site/public/datasets/viper/index.html b/site/public/datasets/viper/index.html index 6f646bb8..bc4ddd3d 100644 --- a/site/public/datasets/viper/index.html +++ b/site/public/datasets/viper/index.html @@ -28,7 +28,7 @@
VIPeR is a person re-identification dataset of images captured at UC Santa Cruz in 2007
VIPeR contains 1,264 images and 632 persons on the UC Santa Cruz campus and is used to train person re-identification algorithms for surveillance
VIPeR Dataset
-
+

Published

2007

@@ -46,10 +46,60 @@

Website

ucsc.edu
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[ page under development ]
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[ page under development ]

VIPeR (Viewpoint Invariant Pedestrian Recognition) is a dataset of pedestrian images captured at University of California Santa Cruz in 2007. Accoriding to the reserachers 2 "cameras were placed in different locations in an academic setting and subjects were notified of the presence of cameras, but were not coached or instructed in any way."

VIPeR is amongst the most widely used publicly available person re-identification datasets. In 2017 the VIPeR dataset was combined into a larger person re-identification created by the Chinese University of Hong Kong called PETA (PEdesTrian Attribute).
-
{% include 'dashboard.html' %}
+
+
Who used VIPeR?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how VIPeR has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Viewpoint Invariant Pedestrian Recognition was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
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+
Academic
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Commercial
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Military / Government
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Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +

diff --git a/site/public/datasets/youtube_celebrities/index.html b/site/public/datasets/youtube_celebrities/index.html index c491e6af..69b3a02e 100644 --- a/site/public/datasets/youtube_celebrities/index.html +++ b/site/public/datasets/youtube_celebrities/index.html @@ -27,9 +27,59 @@
YouTube Celebrities
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[ page under development ]
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{% include 'dashboard.html' %}
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Notes...
+
[ page under development ]
+
+
Who used YouTube Celebrities?
+ +
+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +
+ +
+ +
+ +
+
+ +
+
+
+ +
+ +
Biometric Trade Routes
+ +
+ To help understand how YouTube Celebrities has been used around the world by commercial, military, and academic organizations; existing publicly available research citing YouTube Celebrities was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +
+ +
+ +
+
+
+ +
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Academic
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Commercial
+
Military / Government
+
+
Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.
+
+ + +
+ +
Dataset Citations
+
+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +
+ +
+
Notes...
Selected dataset sequences: (a) MBGC, (b) CMU MoBo, (c) First Honda/UCSD, and (d) YouTube Celebrities.
-- cgit v1.2.3-70-g09d2 From 43c3e3904f80eb56769fba4634729d0e567f9a32 Mon Sep 17 00:00:00 2001 From: adamhrv Date: Wed, 17 Apr 2019 16:51:44 +0200 Subject: update duke --- site/content/pages/datasets/duke_mtmc/index.md | 71 ++++--- site/public/about/assets/LICENSE/index.html | 22 +- site/public/about/attribution/index.html | 22 +- site/public/about/index.html | 30 +-- site/public/about/legal/index.html | 22 +- site/public/about/press/index.html | 22 +- .../datasets/50_people_one_question/index.html | 22 +- site/public/datasets/afad/index.html | 22 +- site/public/datasets/brainwash/index.html | 23 +- site/public/datasets/caltech_10k/index.html | 22 +- site/public/datasets/celeba/index.html | 22 +- site/public/datasets/cofw/index.html | 22 +- site/public/datasets/duke_mtmc/index.html | 231 +++++++++++++++++---- site/public/datasets/feret/index.html | 22 +- site/public/datasets/hrt_transgender/index.html | 22 +- site/public/datasets/index.html | 24 +-- site/public/datasets/lfpw/index.html | 22 +- site/public/datasets/lfw/index.html | 22 +- site/public/datasets/market_1501/index.html | 22 +- site/public/datasets/msceleb/index.html | 22 +- site/public/datasets/oxford_town_centre/index.html | 22 +- site/public/datasets/pipa/index.html | 22 +- site/public/datasets/pubfig/index.html | 22 +- site/public/datasets/uccs/index.html | 33 ++- site/public/datasets/vgg_face2/index.html | 22 +- site/public/datasets/viper/index.html | 22 +- .../public/datasets/youtube_celebrities/index.html | 22 +- site/public/info/index.html | 22 +- site/public/research/00_introduction/index.html | 22 +- .../research/01_from_1_to_100_pixels/index.html | 22 +- .../research/02_what_computers_can_see/index.html | 22 +- site/public/research/index.html | 22 +- site/public/test/chart/index.html | 22 +- site/public/test/citations/index.html | 22 +- site/public/test/csv/index.html | 22 +- site/public/test/datasets/index.html | 22 +- site/public/test/face_search/index.html | 22 +- site/public/test/gallery/index.html | 22 +- site/public/test/index.html | 22 +- site/public/test/map/index.html | 22 +- site/public/test/name_search/index.html | 22 +- site/public/test/pie_chart/index.html | 22 +- 42 files changed, 686 insertions(+), 518 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index ac0a3f2e..2a8bfe05 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -18,35 +18,63 @@ authors: Adam Harvey ### sidebar ### end sidebar -[ page under development ] +Duke MTMC (Multi-Target, Multi-Camera) is a dataset of surveillance video footage taken on Duke University's campus in 2014 and is used for research and development of video tracking systems, person re-identification, and low-resolution facial recognition. The dataset contains over 14 hours of synchronized surveillance video from 8 cameras at 1080p and 60FPS with over 2 million frames of 2,000 students walking to and from classes. The 8 surveillance cameras deployed on campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy"[^duke_mtmc_orig]. -Duke MTMC (Multi-Target, Multi-Camera Tracking) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking algorithms are used for citywide dragnet surveillance systems such as those used throughout China by SenseTime[^sensetime_qz] and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets[^sensenets_uyghurs]. In fact researchers from both SenseTime[^sensetime1] [^sensetime2] and SenseNets[^sensenets_sensetime] used the Duke MTMC dataset for their research. +In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers with explicit and direct links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims. -In this investigation into the Duke MTMC dataset, we found that researchers at Duke University in Durham, North Carolina captured over 2,000 students, faculty members, and passersby into one of the most prolific public surveillance research datasets that's used around the world by commercial and defense surveillance organizations. +In one 2018 [paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.pdf) jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled [Attention-Aware Compositional Network for Person Re-identification](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e), the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. [^sensetime_qz][^sensenets_uyghurs][^xinjiang_nyt] -Since it's publication in 2016, the Duke MTMC dataset has been used in over 100 studies at organizations around the world including SenseTime[^sensetime1] [^sensetime2], SenseNets[^sensenets_sensetime], IARPA and IBM[^iarpa_ibm], Chinese National University of Defense [^cn_defense1][^cn_defense2], US Department of Homeland Security[^us_dhs], Tencent, Microsoft, Microsft Asia, Fraunhofer, Senstar Corp., Alibaba, Naver Labs, Google and Hewlett-Packard Labs to name only a few. +![caption: A collection of 1,600 out of the approximately 2,000 students and pedestrians in the Duke MTMC dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification, and eventually the QMUL SurvFace face recognition dataset. Open Data Commons Attribution License.](assets/duke_mtmc_reid_montage.jpg) -The creation and publication of the Duke MTMC dataset in 2014 (published in 2016) was originally funded by the U.S. Army Research Laboratory and the National Science Foundation[^duke_mtmc_orig]. Though our analysis of the geographic locations of the publicly available research shows over twice as many citations by researchers from China (44% China, 20% United States). In 2018 alone, there were 70 research project citations from China. +Despite [repeated](https://www.hrw.org/news/2017/11/19/china-police-big-data-systems-violate-privacy-target-dissent) [warnings](https://www.hrw.org/news/2018/02/26/china-big-data-fuels-crackdown-minority-region) by Human Rights Watch that the authoritarian surveillance used in China represents a violation of human rights, researchers at Duke University continued to provide open access to their dataset for anyone to use for any project. As the surveillance crisis in China grew, so did the number of citations with links to organizations complicit in the crisis. In 2018 alone there were over 70 research projects happening in China that publicly acknowledged benefiting from the Duke MTMC dataset. Amongst these were projects from SenseNets, SenseTime, CloudWalk, Megvii, Beihang University, and the PLA's National University of Defense Technology. -![caption: A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research and development dataset on . These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. Open Data Commons Attribution License.](assets/duke_mtmc_reid_montage.jpg) +| Organization | Paper | Link | Year | Used Duke MTMC | +|---|---|---|---| +| SenseNets, SenseTime | Attention-Aware Compositional Network for Person Re-identification | [SemanticScholar](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e) | 2018 | ✔ | +|SenseTime| End-to-End Deep Kronecker-Product Matching for Person Re-identification | [thcvf.com](http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf) | 2018| ✔ | +|CloudWalk| Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/pdf/1804.05275.pdf) | 20xx | ✔ | +| Megvii | Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project | [SemanticScholar](https://www.semanticscholar.org/paper/Multi-Target%2C-Multi-Camera-Tracking-by-Hierarchical-Zhang-Wu/10c20cf47d61063032dce4af73a4b8e350bf1128) | 2018 | ✔ | +| Megvii | Person Re-Identification (slides) | [github.io](https://zsc.github.io/megvii-pku-dl-course/slides/Lecture%2011,%20Human%20Understanding_%20ReID%20and%20Pose%20and%20Attributes%20and%20Activity%20.pdf) | 2017 | ✔ | +| Megvii | SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial PersonRe-Identification | [arxiv.org](https://arxiv.org/abs/1810.06996) | 2018 | ✔ | +| CloudWalk | CloudWalk re-identification technology extends facial biometric tracking with improved accuracy | [BiometricUpdate.com](https://www.biometricupdate.com/201903/cloudwalk-re-identification-technology-extends-facial-biometric-tracking-with-improved-accuracy) | 2018 | ✔ | +| CloudWalk | Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/abs/1804.05275)] | 2018 | ✔ | +| National University of Defense Technology | Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers | [SemanticScholar.org](https://www.semanticscholar.org/paper/Tracking-by-Animation%3A-Unsupervised-Learning-of-He-Liu/e90816e1a0e14ea1e7039e0b2782260999aef786) | 2018 | ✔ | +| National University of Defense Technology | Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks | [SemanticScholar.org](https://www.semanticscholar.org/paper/Unsupervised-Multi-Object-Detection-for-Video-Using-He-He/59f357015054bab43fb8cbfd3f3dbf17b1d1f881) | 2018 | ✔ | +| Beihang University | Orientation-Guided Similarity Learning for Person Re-identification | [ieee.org](https://ieeexplore.ieee.org/document/8545620) | 2018 | ✔ | +| Beihang University | Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology | [acm.org](https://dl.acm.org/citation.cfm?id=3240663) | 2018 | ✔ | -The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy".[^duke_mtmc_orig]. Camera 5 was positioned to capture students as entering and exiting the university's main chapel. Each camera's location and approximate field of view. The heat map visualization shows the locations where pedestrians were most frequently annotated in each video from the Duke MTMC dataset. +The reasons that companies in China use the Duke MTMC dataset for research are technically no different than the reasons it is used in the United States and Europe. In fact the original creators of the dataset published a follow up report in 2017 titled [Tracking Social Groups Within and Across Cameras](https://www.semanticscholar.org/paper/Tracking-Social-Groups-Within-and-Across-Cameras-Solera-Calderara/9e644b1e33dd9367be167eb9d832174004840400) with specific applications to "automated analysis of crowds and social gatherings for surveillance and security applications". Their work, as well as the creation of the original dataset in 2014 were both supported in part by the United States Army Research Laboratory. -![caption: Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.](assets/duke_mtmc_camera_map.jpg) +Citations from the United States and Europe show a similar trend to that in China, including publicly acknowledged and verified usage of the Duke MTMC dataset supported or carried out by the United States Department of Homeland Security, IARPA, IBM, Microsoft (who provides surveillance to ICE), and Vision Semantics (who works with the UK Ministry of Defence). One [paper](https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf) is even jointly published by researchers affiliated with both the University College of London and the National University of Defense Technology in China. -![caption: Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_cameras.jpg) +| Organization | Paper | Link | Year | Used Duke MTMC | +|---|---|---|---| +| IARPA, IBM, CloudWalk | Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/abs/1804.05275) | 2018 | ✔ | +| Microsoft | ReXCam: Resource-Efficient, Cross-CameraVideo Analytics at Enterprise Scale | [arxiv.org](https://arxiv.org/abs/1811.01268) | 2018 | ✔ | +| Microsoft | Scaling Video Analytics Systems to Large Camera Deployments | [arxiv.org](https://arxiv.org/pdf/1809.02318.pdf) | 2018 | ✔ | +| University College of London, National University of Defense Technology | Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention Networks | [PDF](https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf) | 2018 | ✔ | +| Vision Semantics Ltd. | Unsupervised Person Re-identification by Deep Learning Tracklet Association | [arxiv.org](https://arxiv.org/abs/1809.02874) | 2018 | ✔ | +| US Dept. of Homeland Security | Re-Identification with Consistent Attentive Siamese Networks | [arxiv.org](https://arxiv.org/abs/1811.07487/) | 2019 | ✔ | + + +By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original [intent](http://vision.cs.duke.edu/DukeMTMC/) of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking". + +The same logic applies for all the new extensions of the Duke MTMC dataset including [Duke MTMC Re-ID](https://github.com/layumi/DukeMTMC-reID_evaluation), [Duke MTMC Video Re-ID](https://github.com/Yu-Wu/DukeMTMC-VideoReID), Duke MTMC Groups, and [Duke MTMC Attribute](https://github.com/vana77/DukeMTMC-attribute). And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called [QMUL-SurvFace](https://qmul-survface.github.io/), which was funded in part by [SeeQuestor](https://seequestor.com), a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, companies, and defense agencies using these datasets to advance their organization's work, Duke MTMC contributes value their their bottom line. Regardless of who is using these datasets or how they're used, they are simple provided to make networks of surveillance cameras more powerful. ![caption: Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_saliencies.jpg) +But from a privacy and human rights perspective the creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset. + +For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies. + {% include 'dashboard.html' %} {% include 'supplementary_header.html' %} -#### Funding +![caption: Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.](assets/duke_mtmc_camera_map.jpg) -Original funding for the Duke MTMC dataset was provided by the Army Research Office under Grant No. W911NF-10-1-0387 and by the National Science Foundation -under Grants IIS-10-17017 and IIS-14-20894. +![caption: Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_cameras.jpg) #### Video Timestamps @@ -73,16 +101,10 @@ The video timestamps contain the likely, but not yet confirmed, date and times o === end columns -### Opting Out - -If you attended Duke University and were captured by any of the 8 surveillance cameras positioned on campus in 2014, there is unfortunately no way to be removed. The dataset files have been distributed throughout the world and it would not be possible to contact all the owners for removal. Nor do the authors provide any options for students to opt-out, nor did they even inform students they would be used at test subjects for surveillance research and development in a project funded, in part, by the United States Army Research Office. - #### Notes - The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812 -{% include 'cite_our_work.html' %} - If you use any data from the Duke MTMC please follow their [license](http://vision.cs.duke.edu/DukeMTMC/#how-to-cite) and cite their work as:
@@ -94,19 +116,16 @@ If you use any data from the Duke MTMC please follow their [license](http://visi }
+{% include 'cite_our_work.html' %} + + #### ToDo - clean up citations, formatting ### Footnotes +[^xinjiang_nyt]: Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html. April 14, 2019. [^sensetime_qz]: [^sensenets_uyghurs]: -[^sensenets_sensetime]: "Attention-Aware Compositional Network for Person Re-identification". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e), [PDF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.pdf) -[^sensetime1]: "End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/End-to-End-Deep-Kronecker-Product-Matching-for-Shen-Xiao/947954cafdefd471b75da8c3bb4c21b9e6d57838), [PDF](http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf) -[^sensetime2]: "Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Person-Re-identification-with-Deep-Graph-Neural-Shen-Li/08d2a558ea2deb117dd8066e864612bf2899905b) -[^duke_mtmc_orig]: "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. [SemanticScholar](https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c) -[^cn_defense1]: "Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Tracking-by-Animation%3A-Unsupervised-Learning-of-He-Liu/e90816e1a0e14ea1e7039e0b2782260999aef786) -[^cn_defense2]: "Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Unsupervised-Multi-Object-Detection-for-Video-Using-He-He/59f357015054bab43fb8cbfd3f3dbf17b1d1f881) -[^iarpa_ibm]: "Horizontal Pyramid Matching for Person Re-identification". 2019. [SemanticScholar](https://www.semanticscholar.org/paper/Horizontal-Pyramid-Matching-for-Person-Fu-Wei/c2a5f27d97744bc1f96d7e1074395749e3c59bc8) -[^us_dhs]: "Re-Identification with Consistent Attentive Siamese Networks". 2018. [SemanticScholar](https://www.semanticscholar.org/paper/Re-Identification-with-Consistent-Attentive-Siamese-Zheng-Karanam/24d6d3adf2176516ef0de2e943ce2084e27c4f94) \ No newline at end of file +[^duke_mtmc_orig]: "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. [SemanticScholar](https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c) \ No newline at end of file diff --git a/site/public/about/assets/LICENSE/index.html b/site/public/about/assets/LICENSE/index.html index 0d3a7878..66d8b3ac 100644 --- a/site/public/about/assets/LICENSE/index.html +++ b/site/public/about/assets/LICENSE/index.html @@ -40,17 +40,17 @@
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- MegaPixels.cc - Datasets - About - Press - Legal and Privacy -
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diff --git a/site/public/about/attribution/index.html b/site/public/about/attribution/index.html index 0a1b8e0f..5fe92b8d 100644 --- a/site/public/about/attribution/index.html +++ b/site/public/about/attribution/index.html @@ -60,17 +60,17 @@ To Adapt: To modify, transform and build upon the database

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diff --git a/site/public/about/index.html b/site/public/about/index.html index 4a4ab3c6..b83736d3 100644 --- a/site/public/about/index.html +++ b/site/public/about/index.html @@ -35,7 +35,8 @@
Legal / Privacy
MegaPixels is an independent art and research project by Adam Harvey and Jules LaPlace that investigates the ethics, origins, and individual privacy implications of face recognition image datasets and their role in the expansion of biometric surveillance technologies.
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The MegaPixels site is made possible with support from Mozilla
+
MegaPixels is made possible with support from Mozilla, our primary funding partner.
+
Additional support for MegaPixels is provided by the European ARTificial Intelligence Network (AI LAB) at the Ars Electronica Center, 1-year research-in-residence grant from Karlsruhe HfG, and sales from the Privacy Gift Shop.

Adam Harvey
@@ -75,6 +76,11 @@ You are free:
PDFMiner.Six and Pandas for research paper data analysis
Please direct questions, comments, or feedback to mastodon.social/@adamhrv
+
Funding Partners
+
The MegaPixels website, research, and development is made possible with support form Mozilla, our primary funding partner.
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[ add logos ]
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Additional support is provided by the European ARTificial Intelligence Network (AI LAB) at the Ars Electronica Center and a 1-year research-in-residence grant from Karlsruhe HfG.
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[ add logos ]

Attribution

If you use MegaPixels or any data derived from it for your work, please cite our original work as follows:
@@ -89,17 +95,17 @@ You are free:
 
   
   
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diff --git a/site/public/about/legal/index.html b/site/public/about/legal/index.html
index 9eb5dd5a..ce10014a 100644
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index 7b0a3e87..70caf03c 100644
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@@ -41,17 +41,17 @@
 
   
   
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diff --git a/site/public/datasets/50_people_one_question/index.html b/site/public/datasets/50_people_one_question/index.html
index dc7919f7..79411122 100644
--- a/site/public/datasets/50_people_one_question/index.html
+++ b/site/public/datasets/50_people_one_question/index.html
@@ -96,17 +96,17 @@
 
   
   
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diff --git a/site/public/datasets/afad/index.html b/site/public/datasets/afad/index.html
index f2b0a5ba..7969c1d6 100644
--- a/site/public/datasets/afad/index.html
+++ b/site/public/datasets/afad/index.html
@@ -109,17 +109,17 @@ Motivation
 
   
   
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diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html
index 95f0d77d..becc8949 100644
--- a/site/public/datasets/brainwash/index.html
+++ b/site/public/datasets/brainwash/index.html
@@ -50,6 +50,7 @@
     Website
     stanford.edu
   Brainwash is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe" 1 captured at 100 second intervals throught the entire day. Brainwash dataset was captured during 3 days in 2014: October 27, November 13, and November 24. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com. 2
+People's Liberation Army National University of Defense Science and Technology
 Brainwash is not a widely used dataset but since its publication by Stanford University in 2015, it has notably appeared in several research papers from the National University of Defense Technology in Changsha, China. In 2016 and in 2017 researchers there conducted studies on detecting people's heads in crowded scenes for the purpose of surveillance.  3  4
 If you happen to have been at Brainwash cafe in San Francisco at any time on October 26, November 13, or November 24 in 2014 you are most likely included in the Brainwash dataset and have unwittingly contributed to surveillance research.
 
@@ -145,17 +146,17 @@
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- MegaPixels.cc - Datasets - About - Press - Legal and Privacy -
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diff --git a/site/public/datasets/caltech_10k/index.html b/site/public/datasets/caltech_10k/index.html index 04d63ee3..abb55148 100644 --- a/site/public/datasets/caltech_10k/index.html +++ b/site/public/datasets/caltech_10k/index.html @@ -106,17 +106,17 @@
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diff --git a/site/public/datasets/celeba/index.html b/site/public/datasets/celeba/index.html index c72f3798..a4a7efa2 100644 --- a/site/public/datasets/celeba/index.html +++ b/site/public/datasets/celeba/index.html @@ -108,17 +108,17 @@
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- MegaPixels.cc - Datasets - About - Press - Legal and Privacy -
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diff --git a/site/public/datasets/cofw/index.html b/site/public/datasets/cofw/index.html index eef8cf5e..c6d7417e 100644 --- a/site/public/datasets/cofw/index.html +++ b/site/public/datasets/cofw/index.html @@ -161,17 +161,17 @@ To increase the number of training images, and since COFW has the exact same la
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diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 5cb6fb0c..48c90d66 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -46,13 +46,167 @@

Website

duke.edu
-
[ page under development ]
-
Duke MTMC (Multi-Target, Multi-Camera Tracking) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking algorithms are used for citywide dragnet surveillance systems such as those used throughout China by SenseTime 1 and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets 2. In fact researchers from both SenseTime 4 5 and SenseNets 3 used the Duke MTMC dataset for their research.
-
In this investigation into the Duke MTMC dataset, we found that researchers at Duke University in Durham, North Carolina captured over 2,000 students, faculty members, and passersby into one of the most prolific public surveillance research datasets that's used around the world by commercial and defense surveillance organizations.
-
Since it's publication in 2016, the Duke MTMC dataset has been used in over 100 studies at organizations around the world including SenseTime 4 5, SenseNets 3, IARPA and IBM 9, Chinese National University of Defense 7 8, US Department of Homeland Security 10, Tencent, Microsoft, Microsft Asia, Fraunhofer, Senstar Corp., Alibaba, Naver Labs, Google and Hewlett-Packard Labs to name only a few.
-
The creation and publication of the Duke MTMC dataset in 2014 (published in 2016) was originally funded by the U.S. Army Research Laboratory and the National Science Foundation 6. Though our analysis of the geographic locations of the publicly available research shows over twice as many citations by researchers from China (44% China, 20% United States). In 2018 alone, there were 70 research project citations from China.
-
A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research and development dataset on . These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. Open Data Commons Attribution License.
The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy". 6. Camera 5 was positioned to capture students as entering and exiting the university's main chapel. Each camera's location and approximate field of view. The heat map visualization shows the locations where pedestrians were most frequently annotated in each video from the Duke MTMC dataset.
-
Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.
Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc
+
Duke MTMC (Multi-Target, Multi-Camera) is a dataset of surveillance video footage taken on Duke University's campus in 2014 and is used for research and development of video tracking systems, person re-identification, and low-resolution facial recognition. The dataset contains over 14 hours of synchronized surveillance video from 8 cameras at 1080p and 60FPS with over 2 million frames of 2,000 students walking to and from classes. The 8 surveillance cameras deployed on campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy" 4.
+
In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers with explicit and direct links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims.
+
In one 2018 paper jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled Attention-Aware Compositional Network for Person Re-identification, the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. 2 3 1
+
A collection of 1,600 out of the approximately 2,000 students and pedestrians in the Duke MTMC dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification, and eventually the QMUL SurvFace face recognition dataset. Open Data Commons Attribution License.
Despite repeated warnings by Human Rights Watch that the authoritarian surveillance used in China represents a violation of human rights, researchers at Duke University continued to provide open access to their dataset for anyone to use for any project. As the surveillance crisis in China grew, so did the number of citations with links to organizations complicit in the crisis. In 2018 alone there were over 70 research projects happening in China that publicly acknowledged benefiting from the Duke MTMC dataset. Amongst these were projects from SenseNets, SenseTime, CloudWalk, Megvii, Beihang University, and the PLA's National University of Defense Technology.
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Organization Paper Link Year Used Duke MTMC
SenseNets, SenseTime Attention-Aware Compositional Network for Person Re-identification SemanticScholar 2018 ✔
SenseTime End-to-End Deep Kronecker-Product Matching for Person Re-identification thcvf.com 2018 ✔
CloudWalk Horizontal Pyramid Matching for Person Re-identification arxiv.org 20xx ✔
Megvii Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project SemanticScholar 2018 ✔
Megvii Person Re-Identification (slides) github.io 2017 ✔
Megvii SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial PersonRe-Identification arxiv.org 2018 ✔
CloudWalk CloudWalk re-identification technology extends facial biometric tracking with improved accuracy BiometricUpdate.com 2018 ✔
CloudWalk Horizontal Pyramid Matching for Person Re-identification arxiv.org] 2018 ✔
National University of Defense Technology Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers SemanticScholar.org 2018 ✔
National University of Defense Technology Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks SemanticScholar.org 2018 ✔
Beihang University Orientation-Guided Similarity Learning for Person Re-identification ieee.org 2018 ✔
Beihang University Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology acm.org 2018 ✔
+
The reasons that companies in China use the Duke MTMC dataset for research are technically no different than the reasons it is used in the United States and Europe. In fact the original creators of the dataset published a follow up report in 2017 titled Tracking Social Groups Within and Across Cameras with specific applications to "automated analysis of crowds and social gatherings for surveillance and security applications". Their work, as well as the creation of the original dataset in 2014 were both supported in part by the United States Army Research Laboratory.
+
Citations from the United States and Europe show a similar trend to that in China, including publicly acknowledged and verified usage of the Duke MTMC dataset supported or carried out by the United States Department of Homeland Security, IARPA, IBM, Microsoft (who provides surveillance to ICE), and Vision Semantics (who works with the UK Ministry of Defence). One paper is even jointly published by researchers affiliated with both the University College of London and the National University of Defense Technology in China.
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Organization Paper Link Year Used Duke MTMC
IARPA, IBM, CloudWalk Horizontal Pyramid Matching for Person Re-identification arxiv.org 2018 ✔
Microsoft ReXCam: Resource-Efficient, Cross-CameraVideo Analytics at Enterprise Scale arxiv.org 2018 ✔
Microsoft Scaling Video Analytics Systems to Large Camera Deployments arxiv.org 2018 ✔
University College of London, National University of Defense Technology Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention Networks PDF 2018 ✔
Vision Semantics Ltd. Unsupervised Person Re-identification by Deep Learning Tracklet Association arxiv.org 2018 ✔
US Dept. of Homeland Security Re-Identification with Consistent Attentive Siamese Networks arxiv.org 2019 ✔
+
By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original intent of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking".
+
The same logic applies for all the new extensions of the Duke MTMC dataset including Duke MTMC Re-ID, Duke MTMC Video Re-ID, Duke MTMC Groups, and Duke MTMC Attribute. And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called QMUL-SurvFace, which was funded in part by SeeQuestor, a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, companies, and defense agencies using these datasets to advance their organization's work, Duke MTMC contributes value their their bottom line. Regardless of who is using these datasets or how they're used, they are simple provided to make networks of surveillance cameras more powerful.
+
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc
But from a privacy and human rights perspective the creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset.
+
For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies.
+

Who used Duke MTMC Dataset?

@@ -112,10 +266,7 @@
Supplementary Information
-
Funding
-
Original funding for the Duke MTMC dataset was provided by the Army Research Office under Grant No. W911NF-10-1-0387 and by the National Science Foundation -under Grants IIS-10-17017 and IIS-14-20894.
-
Video Timestamps
+
Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.
Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc
Video Timestamps

The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least aligns the relative time. The rainy weather on that day also contribute towards the likelihood of March 14, 2014..

@@ -187,13 +338,19 @@ under Grants IIS-10-17017 and IIS-14-20894.

-
Opting Out
-
If you attended Duke University and were captured by any of the 8 surveillance cameras positioned on campus in 2014, there is unfortunately no way to be removed. The dataset files have been distributed throughout the world and it would not be possible to contact all the owners for removal. Nor do the authors provide any options for students to opt-out, nor did they even inform students they would be used at test subjects for surveillance research and development in a project funded, in part, by the United States Army Research Office.
-
Notes
+
Notes

The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812

-
+
If you use any data from the Duke MTMC please follow their license and cite their work as:
+
+@inproceedings{ristani2016MTMC,
+ title =        {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
+ author =       {Ristani, Ergys and Solera, Francesco and Zou, Roger and Cucchiara, Rita and Tomasi, Carlo},
+ booktitle =    {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking},
+ year =         {2016}
+}
+
Cite Our Work

@@ -210,43 +367,29 @@ under Grants IIS-10-17017 and IIS-14-20894.
}
-
If you use any data from the Duke MTMC please follow their license and cite their work as:
-
-@inproceedings{ristani2016MTMC,
- title =        {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
- author =       {Ristani, Ergys and Solera, Francesco and Zou, Roger and Cucchiara, Rita and Tomasi, Carlo},
- booktitle =    {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking},
- year =         {2016}
-}
-
ToDo
+
ToDo

clean up citations, formatting

-
References
a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
+
References
a
Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html. April 14, 2019.
+
a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/

a
https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/
-
a b
"Attention-Aware Compositional Network for Person Re-identification". 2018. SemanticScholar, PDF
-
a b
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. SemanticScholar, PDF
-
a b
"Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. SemanticScholar
-
a b
"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. SemanticScholar
-
a
"Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. SemanticScholar
-
a
"Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. SemanticScholar
-
a
"Horizontal Pyramid Matching for Person Re-identification". 2019. SemanticScholar
-
a
"Re-Identification with Consistent Attentive Siamese Networks". 2018. SemanticScholar
+
a
"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. SemanticScholar

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diff --git a/site/public/datasets/feret/index.html b/site/public/datasets/feret/index.html index 387826b0..7f9ed94c 100644 --- a/site/public/datasets/feret/index.html +++ b/site/public/datasets/feret/index.html @@ -119,17 +119,17 @@
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diff --git a/site/public/datasets/hrt_transgender/index.html b/site/public/datasets/hrt_transgender/index.html index 6b9ae7be..4e566a4a 100644 --- a/site/public/datasets/hrt_transgender/index.html +++ b/site/public/datasets/hrt_transgender/index.html @@ -49,17 +49,17 @@
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diff --git a/site/public/datasets/index.html b/site/public/datasets/index.html index 75961089..5c8e2546 100644 --- a/site/public/datasets/index.html +++ b/site/public/datasets/index.html @@ -28,7 +28,7 @@
Facial Recognition Datasets
-
Explore publicly available facial recognition datasets. More datasets will be added throughout 2019.
+
Explore publicly available facial recognition datasets feeding into research and development of biometric surveillance technologies at the largest technology companies and defense contractors in the world.

@@ -115,17 +115,17 @@
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diff --git a/site/public/datasets/lfpw/index.html b/site/public/datasets/lfpw/index.html index 45de2599..a9eb025d 100644 --- a/site/public/datasets/lfpw/index.html +++ b/site/public/datasets/lfpw/index.html @@ -98,17 +98,17 @@
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diff --git a/site/public/datasets/market_1501/index.html b/site/public/datasets/market_1501/index.html index 7c545335..05750dc7 100644 --- a/site/public/datasets/market_1501/index.html +++ b/site/public/datasets/market_1501/index.html @@ -114,17 +114,17 @@ organization={Springer}
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diff --git a/site/public/datasets/pubfig/index.html b/site/public/datasets/pubfig/index.html index e81e12bc..ef289954 100644 --- a/site/public/datasets/pubfig/index.html +++ b/site/public/datasets/pubfig/index.html @@ -99,17 +99,17 @@
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diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html index 32f7cdb2..9347d536 100644 --- a/site/public/datasets/uccs/index.html +++ b/site/public/datasets/uccs/index.html @@ -51,12 +51,11 @@
uccs.edu

UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications" 1. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge". 3 In this investigation, we examine the contents of the dataset, funding sources, photo EXIF data, and information from publicly available research project citations.

The UCCS dataset includes over 1,700 unique identities, most of which are students walking to and from class. As of 2018, it was the "largest surveillance [face recognition] benchmark in the public domain." 4 The photos were taken during the spring semesters of 2012 – 2013 on the West Lawn of the University of Colorado Colorado Springs campus. The photographs were timed to capture students during breaks between their scheduled classes in the morning and afternoon during Monday through Thursday. "For example, a student taking Monday-Wednesday classes at 12:30 PM will show up in the camera on almost every Monday and Wednesday." 2.
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Example images from the UnConstrained College Students Dataset.
The long-range surveillance images in the UnContsrained College Students dataset were captured using a Canon 7D 18 megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured." 2 -Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face detection and recognition systems for real world applications in defense, intelligence, and commercial applications.
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The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps
In the two papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), the researchers disclosed their funding sources as ODNI (United States Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. Further, UCCS's VAST site explicity states they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests.
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The EXIF data embedded in the images shows that the photo capture times follow a similar pattern, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken on Friday shows that the researchers were only interested in capturing images of students.
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UCCS photos captured per weekday © megapixels.cc
UCCS photos captured per weekday © megapixels.cc
The two research papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), Small Business Innovation Research (SBIR), Special Operations Command and Small Business Innovation Research (SOCOM SBIR), and the National Science Foundation. Further, UCCS's VAST site explicitly states they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests, clearly establishing the the funding sources and immediate benefactors of this dataset are United States defense and intelligence agencies.
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Although the images were first captured in 2012 – 2013 the dataset was not publicly released until 2016. Then in 2017 the UCCS face dataset formed the basis for a defense and intelligence agency funded face recognition challenge project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the 2nd Unconstrained Face Detection and Open Set Recognition Challenge at the European Computer Vision Conference (ECCV) in Munich, Germany.
+
Example images from the UnConstrained College Students Dataset.
The long-range surveillance images in the UnContsrained College Students dataset were taken using a Canon 7D 18-megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured." 2 +Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face recognition systems for real world applications in defense, intelligence, and commercial sectors.
+
The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps
The EXIF data embedded in the images shows that the photo capture times follow a similar pattern, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken on Friday shows that the researchers were only interested in capturing images of students.
+
UCCS photos captured per weekday © megapixels.cc
UCCS photos captured per weekday © megapixels.cc
The two research papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContsrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), and the Special Operations Command and Small Business Innovation Research (SOCOM SBIR) amongst others. UCCS's VAST site also explicitly states that they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests, clearly establishing the the funding sources and immediate benefactors of this dataset are United States defense and intelligence agencies.
+
Although the images were first captured in 2012 – 2013 the dataset was not publicly released until 2016. In 2017 the UCCS face dataset formed the basis for a defense and intelligence agency funded face recognition challenge project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the 2nd Unconstrained Face Detection and Open Set Recognition Challenge at the European Computer Vision Conference (ECCV) in Munich, Germany.

As of April 15, 2019, the UCCS dataset is no longer available for public download. But during the three years it was publicly available (2016-2019) the UCCS dataset appeared in at least 6 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military.

Who used UCCS?
@@ -259,17 +258,17 @@ Their setup made it impossible for students to know they were being photographed
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diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html index a9d318f1..24ce4b2d 100644 --- a/site/public/datasets/vgg_face2/index.html +++ b/site/public/datasets/vgg_face2/index.html @@ -124,17 +124,17 @@
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diff --git a/site/public/datasets/youtube_celebrities/index.html b/site/public/datasets/youtube_celebrities/index.html index 69b3a02e..e90b45cb 100644 --- a/site/public/datasets/youtube_celebrities/index.html +++ b/site/public/datasets/youtube_celebrities/index.html @@ -95,17 +95,17 @@ the views of our sponsors.
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diff --git a/site/public/research/00_introduction/index.html b/site/public/research/00_introduction/index.html index 353e3270..535958cc 100644 --- a/site/public/research/00_introduction/index.html +++ b/site/public/research/00_introduction/index.html @@ -83,17 +83,17 @@
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diff --git a/site/public/research/01_from_1_to_100_pixels/index.html b/site/public/research/01_from_1_to_100_pixels/index.html index 9426ef0f..fe49e998 100644 --- a/site/public/research/01_from_1_to_100_pixels/index.html +++ b/site/public/research/01_from_1_to_100_pixels/index.html @@ -121,17 +121,17 @@ relying on FaceID and TouchID to protect their information agree to a

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diff --git a/site/public/test/face_search/index.html b/site/public/test/face_search/index.html index 75bb907b..e2db70df 100644 --- a/site/public/test/face_search/index.html +++ b/site/public/test/face_search/index.html @@ -32,17 +32,17 @@
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-- cgit v1.2.3-70-g09d2 From 70bb504bcc12c2ec57fef6c01d9cff677fb1bee3 Mon Sep 17 00:00:00 2001 From: adamhrv Date: Wed, 17 Apr 2019 17:03:24 +0200 Subject: update duke --- site/content/pages/datasets/duke_mtmc/index.md | 14 ++++++++------ site/public/datasets/duke_mtmc/index.html | 10 +++++----- 2 files changed, 13 insertions(+), 11 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index 2a8bfe05..dd4551d9 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -52,29 +52,31 @@ Citations from the United States and Europe show a similar trend to that in Chin | IARPA, IBM, CloudWalk | Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/abs/1804.05275) | 2018 | ✔ | | Microsoft | ReXCam: Resource-Efficient, Cross-CameraVideo Analytics at Enterprise Scale | [arxiv.org](https://arxiv.org/abs/1811.01268) | 2018 | ✔ | | Microsoft | Scaling Video Analytics Systems to Large Camera Deployments | [arxiv.org](https://arxiv.org/pdf/1809.02318.pdf) | 2018 | ✔ | -| University College of London, National University of Defense Technology | Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention Networks | [PDF](https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf) | 2018 | ✔ | +| University College of London, National University of Defense Technology | Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention Networks | [SemanticScholar.org](https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf) | 2018 | ✔ | | Vision Semantics Ltd. | Unsupervised Person Re-identification by Deep Learning Tracklet Association | [arxiv.org](https://arxiv.org/abs/1809.02874) | 2018 | ✔ | | US Dept. of Homeland Security | Re-Identification with Consistent Attentive Siamese Networks | [arxiv.org](https://arxiv.org/abs/1811.07487/) | 2019 | ✔ | By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original [intent](http://vision.cs.duke.edu/DukeMTMC/) of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking". -The same logic applies for all the new extensions of the Duke MTMC dataset including [Duke MTMC Re-ID](https://github.com/layumi/DukeMTMC-reID_evaluation), [Duke MTMC Video Re-ID](https://github.com/Yu-Wu/DukeMTMC-VideoReID), Duke MTMC Groups, and [Duke MTMC Attribute](https://github.com/vana77/DukeMTMC-attribute). And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called [QMUL-SurvFace](https://qmul-survface.github.io/), which was funded in part by [SeeQuestor](https://seequestor.com), a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, companies, and defense agencies using these datasets to advance their organization's work, Duke MTMC contributes value their their bottom line. Regardless of who is using these datasets or how they're used, they are simple provided to make networks of surveillance cameras more powerful. +The same logic applies for all the new extensions of the Duke MTMC dataset including [Duke MTMC Re-ID](https://github.com/layumi/DukeMTMC-reID_evaluation), [Duke MTMC Video Re-ID](https://github.com/Yu-Wu/DukeMTMC-VideoReID), Duke MTMC Groups, and [Duke MTMC Attribute](https://github.com/vana77/DukeMTMC-attribute). And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called [QMUL-SurvFace](https://qmul-survface.github.io/), which was funded in part by [SeeQuestor](https://seequestor.com), a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, security contractors, and defense agencies using these datasets to advance their organization's work, Duke MTMC provides significant value regardless of who else is using it so long as it accelerate advances their own interests in artificial intelligence. ![caption: Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_saliencies.jpg) -But from a privacy and human rights perspective the creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset. + +But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against universal human rights, or against your own political interests. For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies. +![caption: Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_cameras.jpg) + +![caption: Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.](assets/duke_mtmc_camera_map.jpg) + {% include 'dashboard.html' %} {% include 'supplementary_header.html' %} -![caption: Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.](assets/duke_mtmc_camera_map.jpg) - -![caption: Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_cameras.jpg) #### Video Timestamps diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 48c90d66..def7b8fa 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -182,7 +182,7 @@ University College of London, National University of Defense Technology Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention Networks -PDF +SemanticScholar.org 2018 ✔ @@ -203,10 +203,10 @@
By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original intent of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking".
-
The same logic applies for all the new extensions of the Duke MTMC dataset including Duke MTMC Re-ID, Duke MTMC Video Re-ID, Duke MTMC Groups, and Duke MTMC Attribute. And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called QMUL-SurvFace, which was funded in part by SeeQuestor, a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, companies, and defense agencies using these datasets to advance their organization's work, Duke MTMC contributes value their their bottom line. Regardless of who is using these datasets or how they're used, they are simple provided to make networks of surveillance cameras more powerful.
-
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc
But from a privacy and human rights perspective the creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset.
+
The same logic applies for all the new extensions of the Duke MTMC dataset including Duke MTMC Re-ID, Duke MTMC Video Re-ID, Duke MTMC Groups, and Duke MTMC Attribute. And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called QMUL-SurvFace, which was funded in part by SeeQuestor, a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, security contractors, and defense agencies using these datasets to advance their organization's work, Duke MTMC provides significant value regardless of who else is using it so long as it accelerate advances their own interests in artificial intelligence.
+
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc
But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against universal human rights, or against your own political interests.

For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies.
-
+
Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc
Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.

Who used Duke MTMC Dataset?

@@ -266,7 +266,7 @@
Supplementary Information
-
Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.
Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc
Video Timestamps
+
Video Timestamps

The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least aligns the relative time. The rainy weather on that day also contribute towards the likelihood of March 14, 2014..

-- cgit v1.2.3-70-g09d2 From 80901bd8af4f78be8d3e697115f07d4e69473de5 Mon Sep 17 00:00:00 2001 From: adamhrv Date: Wed, 17 Apr 2019 17:15:13 +0200 Subject: update duke --- site/content/pages/datasets/duke_mtmc/index.md | 18 +++++------ site/public/datasets/duke_mtmc/index.html | 41 +++++++++++--------------- 2 files changed, 26 insertions(+), 33 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index dd4551d9..55dd8c2b 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -22,7 +22,7 @@ Duke MTMC (Multi-Target, Multi-Camera) is a dataset of surveillance video footag In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers with explicit and direct links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims. -In one 2018 [paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.pdf) jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled [Attention-Aware Compositional Network for Person Re-identification](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e), the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. [^sensetime_qz][^sensenets_uyghurs][^xinjiang_nyt] +In one 2018 [paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Xu_Attention-Aware_Compositional_Network_CVPR_2018_paper.pdf) jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled [Attention-Aware Compositional Network for Person Re-identification](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e), the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. [^xinjiang_nyt][^sensetime_qz][^sensenets_uyghurs] ![caption: A collection of 1,600 out of the approximately 2,000 students and pedestrians in the Duke MTMC dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification, and eventually the QMUL SurvFace face recognition dataset. Open Data Commons Attribution License.](assets/duke_mtmc_reid_montage.jpg) @@ -32,12 +32,11 @@ Despite [repeated](https://www.hrw.org/news/2017/11/19/china-police-big-data-sys |---|---|---|---| | SenseNets, SenseTime | Attention-Aware Compositional Network for Person Re-identification | [SemanticScholar](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e) | 2018 | ✔ | |SenseTime| End-to-End Deep Kronecker-Product Matching for Person Re-identification | [thcvf.com](http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf) | 2018| ✔ | -|CloudWalk| Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/pdf/1804.05275.pdf) | 20xx | ✔ | +|CloudWalk| Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/pdf/1804.05275.pdf) | 2018 | ✔ | | Megvii | Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project | [SemanticScholar](https://www.semanticscholar.org/paper/Multi-Target%2C-Multi-Camera-Tracking-by-Hierarchical-Zhang-Wu/10c20cf47d61063032dce4af73a4b8e350bf1128) | 2018 | ✔ | | Megvii | Person Re-Identification (slides) | [github.io](https://zsc.github.io/megvii-pku-dl-course/slides/Lecture%2011,%20Human%20Understanding_%20ReID%20and%20Pose%20and%20Attributes%20and%20Activity%20.pdf) | 2017 | ✔ | | Megvii | SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial PersonRe-Identification | [arxiv.org](https://arxiv.org/abs/1810.06996) | 2018 | ✔ | | CloudWalk | CloudWalk re-identification technology extends facial biometric tracking with improved accuracy | [BiometricUpdate.com](https://www.biometricupdate.com/201903/cloudwalk-re-identification-technology-extends-facial-biometric-tracking-with-improved-accuracy) | 2018 | ✔ | -| CloudWalk | Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/abs/1804.05275)] | 2018 | ✔ | | National University of Defense Technology | Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers | [SemanticScholar.org](https://www.semanticscholar.org/paper/Tracking-by-Animation%3A-Unsupervised-Learning-of-He-Liu/e90816e1a0e14ea1e7039e0b2782260999aef786) | 2018 | ✔ | | National University of Defense Technology | Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks | [SemanticScholar.org](https://www.semanticscholar.org/paper/Unsupervised-Multi-Object-Detection-for-Video-Using-He-He/59f357015054bab43fb8cbfd3f3dbf17b1d1f881) | 2018 | ✔ | | Beihang University | Orientation-Guided Similarity Learning for Person Re-identification | [ieee.org](https://ieeexplore.ieee.org/document/8545620) | 2018 | ✔ | @@ -49,10 +48,10 @@ Citations from the United States and Europe show a similar trend to that in Chin | Organization | Paper | Link | Year | Used Duke MTMC | |---|---|---|---| -| IARPA, IBM, CloudWalk | Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/abs/1804.05275) | 2018 | ✔ | +| IARPA, IBM | Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/abs/1804.05275) | 2018 | ✔ | | Microsoft | ReXCam: Resource-Efficient, Cross-CameraVideo Analytics at Enterprise Scale | [arxiv.org](https://arxiv.org/abs/1811.01268) | 2018 | ✔ | | Microsoft | Scaling Video Analytics Systems to Large Camera Deployments | [arxiv.org](https://arxiv.org/pdf/1809.02318.pdf) | 2018 | ✔ | -| University College of London, National University of Defense Technology | Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention Networks | [SemanticScholar.org](https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf) | 2018 | ✔ | +| University College of London | Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention Networks | [SemanticScholar.org](https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf) | 2018 | ✔ | | Vision Semantics Ltd. | Unsupervised Person Re-identification by Deep Learning Tracklet Association | [arxiv.org](https://arxiv.org/abs/1809.02874) | 2018 | ✔ | | US Dept. of Homeland Security | Re-Identification with Consistent Attentive Siamese Networks | [arxiv.org](https://arxiv.org/abs/1811.07487/) | 2019 | ✔ | @@ -66,7 +65,7 @@ The same logic applies for all the new extensions of the Duke MTMC dataset inclu But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against universal human rights, or against your own political interests. -For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies. +For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class in 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies. ![caption: Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_cameras.jpg) @@ -80,7 +79,7 @@ For the approximately 2,000 students in Duke MTMC dataset there is unfortunately #### Video Timestamps -The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop [time sync data](http://vision.cs.duke.edu/DukeMTMC/details.html#time-sync) provided by the researchers, it at least aligns the relative time. The [rainy weather](https://www.wunderground.com/history/daily/KIGX/date/2014-3-19?req_city=Durham&req_state=NC&req_statename=North%20Carolina&reqdb.zip=27708&reqdb.magic=1&reqdb.wmo=99999) on that day also contribute towards the likelihood of March 14, 2014.. +The video timestamps contain the likely, but not yet confirmed, date and times the video recorded. Because the video timestamps align with the start and stop [time sync data](http://vision.cs.duke.edu/DukeMTMC/details.html#time-sync) provided by the researchers, it at least confirms the relative timing. The [[precipitous weather](https://www.wunderground.com/history/daily/KIGX/date/2014-3-19?req_city=Durham&req_state=NC&req_statename=North%20Carolina&reqdb.zip=27708&reqdb.magic=1&reqdb.wmo=99999) on March 14, 2014 in Durham, North Carolina supports, but does not confirm, that this day is a potential capture date. === columns 2 @@ -105,7 +104,9 @@ The video timestamps contain the likely, but not yet confirmed, date and times o #### Notes -- The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812 +- The original Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812, and their own research typically mentions 2,000. For this write up we used 2,000 to describe the approximate number of students. + +{% include 'cite_our_work.html' %} If you use any data from the Duke MTMC please follow their [license](http://vision.cs.duke.edu/DukeMTMC/#how-to-cite) and cite their work as: @@ -118,7 +119,6 @@ If you use any data from the Duke MTMC please follow their [license](http://visi } -{% include 'cite_our_work.html' %} #### ToDo diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index def7b8fa..7b965bd4 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -48,7 +48,7 @@
duke.edu

Duke MTMC (Multi-Target, Multi-Camera) is a dataset of surveillance video footage taken on Duke University's campus in 2014 and is used for research and development of video tracking systems, person re-identification, and low-resolution facial recognition. The dataset contains over 14 hours of synchronized surveillance video from 8 cameras at 1080p and 60FPS with over 2 million frames of 2,000 students walking to and from classes. The 8 surveillance cameras deployed on campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy" 4.

In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers with explicit and direct links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims.
-
In one 2018 paper jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled Attention-Aware Compositional Network for Person Re-identification, the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. 2 3 1
+
In one 2018 paper jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled Attention-Aware Compositional Network for Person Re-identification, the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. 1 2 3

A collection of 1,600 out of the approximately 2,000 students and pedestrians in the Duke MTMC dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification, and eventually the QMUL SurvFace face recognition dataset. Open Data Commons Attribution License.
Despite repeated warnings by Human Rights Watch that the authoritarian surveillance used in China represents a violation of human rights, researchers at Duke University continued to provide open access to their dataset for anyone to use for any project. As the surveillance crisis in China grew, so did the number of citations with links to organizations complicit in the crisis. In 2018 alone there were over 70 research projects happening in China that publicly acknowledged benefiting from the Duke MTMC dataset. Amongst these were projects from SenseNets, SenseTime, CloudWalk, Megvii, Beihang University, and the PLA's National University of Defense Technology.

@@ -78,7 +78,7 @@ - + @@ -110,13 +110,6 @@ - - - - - - - @@ -159,7 +152,7 @@ - + @@ -180,7 +173,7 @@ - + @@ -205,7 +198,7 @@
By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original intent of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking".
The same logic applies for all the new extensions of the Duke MTMC dataset including Duke MTMC Re-ID, Duke MTMC Video Re-ID, Duke MTMC Groups, and Duke MTMC Attribute. And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called QMUL-SurvFace, which was funded in part by SeeQuestor, a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, security contractors, and defense agencies using these datasets to advance their organization's work, Duke MTMC provides significant value regardless of who else is using it so long as it accelerate advances their own interests in artificial intelligence.
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc
But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against universal human rights, or against your own political interests.
-
For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies.
+
For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class in 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies.

Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc
Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.

Who used Duke MTMC Dataset?
@@ -267,7 +260,7 @@
Supplementary Information

Video Timestamps
-
The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least aligns the relative time. The rainy weather on that day also contribute towards the likelihood of March 14, 2014..
+
The video timestamps contain the likely, but not yet confirmed, date and times the video recorded. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least confirms the relative timing. The [precipitous weather on March 14, 2014 in Durham, North Carolina supports, but does not confirm, that this day is a potential capture date.

CloudWalk Horizontal Pyramid Matching for Person Re-identification arxiv.org 20xx 2018 ✔

✔

CloudWalk Horizontal Pyramid Matching for Person Re-identification arxiv.org] 2018 ✔
National University of Defense Technology Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers SemanticScholar.org

IARPA, IBM, CloudWalk IARPA, IBM Horizontal Pyramid Matching for Person Re-identification arxiv.org 2018 ✔

University College of London, National University of Defense Technology University College of London Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention Networks SemanticScholar.org 2018
@@ -340,17 +333,9 @@

Camera
Notes

-
The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812
+
The original Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812, and their own research typically mentions 2,000. For this write up we used 2,000 to describe the approximate number of students.

-
If you use any data from the Duke MTMC please follow their license and cite their work as:
-
-@inproceedings{ristani2016MTMC,
- title =        {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
- author =       {Ristani, Ergys and Solera, Francesco and Zou, Roger and Cucchiara, Rita and Tomasi, Carlo},
- booktitle =    {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking},
- year =         {2016}
-}
-
+

Cite Our Work

@@ -367,7 +352,15 @@ }
-
ToDo
+
If you use any data from the Duke MTMC please follow their license and cite their work as:
+
+@inproceedings{ristani2016MTMC,
+ title =        {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
+ author =       {Ristani, Ergys and Solera, Francesco and Zou, Roger and Cucchiara, Rita and Tomasi, Carlo},
+ booktitle =    {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking},
+ year =         {2016}
+}
+
ToDo

clean up citations, formatting

-- cgit v1.2.3-70-g09d2 From fa11005bed137f90f627eeacc0d264d77206b992 Mon Sep 17 00:00:00 2001 From: adamhrv Date: Wed, 17 Apr 2019 17:44:29 +0200 Subject: update duke, uccs --- site/content/pages/datasets/brainwash/index.md | 5 +- site/content/pages/datasets/duke_mtmc/index.md | 27 ++++---- site/content/pages/datasets/index.md | 2 +- .../pages/datasets/uccs/assets/uccs_grid.jpg | Bin 142280 -> 112588 bytes site/content/pages/datasets/uccs/index.md | 28 +++----- site/public/datasets/duke_mtmc/index.html | 76 ++++++++++----------- site/public/datasets/uccs/index.html | 20 +++--- 7 files changed, 75 insertions(+), 83 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/site/content/pages/datasets/brainwash/index.md b/site/content/pages/datasets/brainwash/index.md index 156b02c7..0b699a7d 100644 --- a/site/content/pages/datasets/brainwash/index.md +++ b/site/content/pages/datasets/brainwash/index.md @@ -21,6 +21,9 @@ authors: Adam Harvey *Brainwash* is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe"[^readme] captured at 100 second intervals throught the entire day. Brainwash dataset was captured during 3 days in 2014: October 27, November 13, and November 24. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com.[^end_to_end] +People's Liberation Army National University of Defense Science and Technology + + Brainwash is not a widely used dataset but since its publication by Stanford University in 2015, it has notably appeared in several research papers from the National University of Defense Technology in Changsha, China. In 2016 and in 2017 researchers there conducted studies on detecting people's heads in crowded scenes for the purpose of surveillance. [^localized_region_context] [^replacement_algorithm] If you happen to have been at Brainwash cafe in San Francisco at any time on October 26, November 13, or November 24 in 2014 you are most likely included in the Brainwash dataset and have unwittingly contributed to surveillance research. @@ -52,4 +55,4 @@ TODO [^readme]: "readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385. [^end_to_end]: Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016. [^localized_region_context]: Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598. -[^replacement_algorithm]: Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering. \ No newline at end of file +[^replacement_algorithm]: Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering. diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index 55dd8c2b..1dd189ac 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -30,17 +30,17 @@ Despite [repeated](https://www.hrw.org/news/2017/11/19/china-police-big-data-sys | Organization | Paper | Link | Year | Used Duke MTMC | |---|---|---|---| -| SenseNets, SenseTime | Attention-Aware Compositional Network for Person Re-identification | [SemanticScholar](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e) | 2018 | ✔ | -|SenseTime| End-to-End Deep Kronecker-Product Matching for Person Re-identification | [thcvf.com](http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf) | 2018| ✔ | +| Beihang University | Orientation-Guided Similarity Learning for Person Re-identification | [ieee.org](https://ieeexplore.ieee.org/document/8545620) | 2018 | ✔ | +| Beihang University | Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology | [acm.org](https://dl.acm.org/citation.cfm?id=3240663) | 2018 | ✔ | +| CloudWalk | CloudWalk re-identification technology extends facial biometric tracking with improved accuracy | [BiometricUpdate.com](https://www.biometricupdate.com/201903/cloudwalk-re-identification-technology-extends-facial-biometric-tracking-with-improved-accuracy) | 2019 | ✔ | |CloudWalk| Horizontal Pyramid Matching for Person Re-identification | [arxiv.org](https://arxiv.org/pdf/1804.05275.pdf) | 2018 | ✔ | -| Megvii | Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project | [SemanticScholar](https://www.semanticscholar.org/paper/Multi-Target%2C-Multi-Camera-Tracking-by-Hierarchical-Zhang-Wu/10c20cf47d61063032dce4af73a4b8e350bf1128) | 2018 | ✔ | | Megvii | Person Re-Identification (slides) | [github.io](https://zsc.github.io/megvii-pku-dl-course/slides/Lecture%2011,%20Human%20Understanding_%20ReID%20and%20Pose%20and%20Attributes%20and%20Activity%20.pdf) | 2017 | ✔ | +| Megvii | Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project | [SemanticScholar](https://www.semanticscholar.org/paper/Multi-Target%2C-Multi-Camera-Tracking-by-Hierarchical-Zhang-Wu/10c20cf47d61063032dce4af73a4b8e350bf1128) | 2018 | ✔ | | Megvii | SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial PersonRe-Identification | [arxiv.org](https://arxiv.org/abs/1810.06996) | 2018 | ✔ | -| CloudWalk | CloudWalk re-identification technology extends facial biometric tracking with improved accuracy | [BiometricUpdate.com](https://www.biometricupdate.com/201903/cloudwalk-re-identification-technology-extends-facial-biometric-tracking-with-improved-accuracy) | 2018 | ✔ | | National University of Defense Technology | Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers | [SemanticScholar.org](https://www.semanticscholar.org/paper/Tracking-by-Animation%3A-Unsupervised-Learning-of-He-Liu/e90816e1a0e14ea1e7039e0b2782260999aef786) | 2018 | ✔ | | National University of Defense Technology | Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks | [SemanticScholar.org](https://www.semanticscholar.org/paper/Unsupervised-Multi-Object-Detection-for-Video-Using-He-He/59f357015054bab43fb8cbfd3f3dbf17b1d1f881) | 2018 | ✔ | -| Beihang University | Orientation-Guided Similarity Learning for Person Re-identification | [ieee.org](https://ieeexplore.ieee.org/document/8545620) | 2018 | ✔ | -| Beihang University | Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology | [acm.org](https://dl.acm.org/citation.cfm?id=3240663) | 2018 | ✔ | +| SenseNets, SenseTime | Attention-Aware Compositional Network for Person Re-identification | [SemanticScholar](https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e) | 2018 | ✔ | +|SenseTime| End-to-End Deep Kronecker-Product Matching for Person Re-identification | [thcvf.com](http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_End-to-End_Deep_Kronecker-Product_CVPR_2018_paper.pdf) | 2018| ✔ | The reasons that companies in China use the Duke MTMC dataset for research are technically no different than the reasons it is used in the United States and Europe. In fact the original creators of the dataset published a follow up report in 2017 titled [Tracking Social Groups Within and Across Cameras](https://www.semanticscholar.org/paper/Tracking-Social-Groups-Within-and-Across-Cameras-Solera-Calderara/9e644b1e33dd9367be167eb9d832174004840400) with specific applications to "automated analysis of crowds and social gatherings for surveillance and security applications". Their work, as well as the creation of the original dataset in 2014 were both supported in part by the United States Army Research Laboratory. @@ -52,8 +52,8 @@ Citations from the United States and Europe show a similar trend to that in Chin | Microsoft | ReXCam: Resource-Efficient, Cross-CameraVideo Analytics at Enterprise Scale | [arxiv.org](https://arxiv.org/abs/1811.01268) | 2018 | ✔ | | Microsoft | Scaling Video Analytics Systems to Large Camera Deployments | [arxiv.org](https://arxiv.org/pdf/1809.02318.pdf) | 2018 | ✔ | | University College of London | Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention Networks | [SemanticScholar.org](https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf) | 2018 | ✔ | -| Vision Semantics Ltd. | Unsupervised Person Re-identification by Deep Learning Tracklet Association | [arxiv.org](https://arxiv.org/abs/1809.02874) | 2018 | ✔ | | US Dept. of Homeland Security | Re-Identification with Consistent Attentive Siamese Networks | [arxiv.org](https://arxiv.org/abs/1811.07487/) | 2019 | ✔ | +| Vision Semantics Ltd. | Unsupervised Person Re-identification by Deep Learning Tracklet Association | [arxiv.org](https://arxiv.org/abs/1809.02874) | 2018 | ✔ | By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original [intent](http://vision.cs.duke.edu/DukeMTMC/) of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking". @@ -79,7 +79,7 @@ For the approximately 2,000 students in Duke MTMC dataset there is unfortunately #### Video Timestamps -The video timestamps contain the likely, but not yet confirmed, date and times the video recorded. Because the video timestamps align with the start and stop [time sync data](http://vision.cs.duke.edu/DukeMTMC/details.html#time-sync) provided by the researchers, it at least confirms the relative timing. The [[precipitous weather](https://www.wunderground.com/history/daily/KIGX/date/2014-3-19?req_city=Durham&req_state=NC&req_statename=North%20Carolina&reqdb.zip=27708&reqdb.magic=1&reqdb.wmo=99999) on March 14, 2014 in Durham, North Carolina supports, but does not confirm, that this day is a potential capture date. +The video timestamps contain the likely, but not yet confirmed, date and times the video recorded. Because the video timestamps align with the start and stop [time sync data](http://vision.cs.duke.edu/DukeMTMC/details.html#time-sync) provided by the researchers, it at least confirms the relative timing. The [precipitous weather](https://www.wunderground.com/history/daily/KIGX/date/2014-3-19?req_city=Durham&req_state=NC&req_statename=North%20Carolina&reqdb.zip=27708&reqdb.magic=1&reqdb.wmo=99999) on March 14, 2014 in Durham, North Carolina supports, but does not confirm, that this day is a potential capture date. === columns 2 @@ -104,7 +104,11 @@ The video timestamps contain the likely, but not yet confirmed, date and times t #### Notes -- The original Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812, and their own research typically mentions 2,000. For this write up we used 2,000 to describe the approximate number of students. +The original Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812, and their own research typically mentions 2,000. For this write up we used 2,000 to describe the approximate number of students. + +#### Ethics + +Please direct any questions about the ethics of the dataset to Duke University's [Institutional Ethics & Compliance Office](https://hr.duke.edu/policies/expectations/compliance/) using the number at the bottom of the page. {% include 'cite_our_work.html' %} @@ -120,11 +124,6 @@ If you use any data from the Duke MTMC please follow their [license](http://visi - -#### ToDo - -- clean up citations, formatting - ### Footnotes [^xinjiang_nyt]: Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html. April 14, 2019. diff --git a/site/content/pages/datasets/index.md b/site/content/pages/datasets/index.md index c0373d60..289aa2fd 100644 --- a/site/content/pages/datasets/index.md +++ b/site/content/pages/datasets/index.md @@ -13,4 +13,4 @@ sync: false # Facial Recognition Datasets -Explore publicly available facial recognition datasets. More datasets will be added throughout 2019. +Explore publicly available facial recognition datasets feeding into research and development of biometric surveillance technologies at the largest technology companies and defense contractors in the world. diff --git a/site/content/pages/datasets/uccs/assets/uccs_grid.jpg b/site/content/pages/datasets/uccs/assets/uccs_grid.jpg index d3d898ea..95dff617 100644 Binary files a/site/content/pages/datasets/uccs/assets/uccs_grid.jpg and b/site/content/pages/datasets/uccs/assets/uccs_grid.jpg differ diff --git a/site/content/pages/datasets/uccs/index.md b/site/content/pages/datasets/uccs/index.md index 68fff4db..b6073384 100644 --- a/site/content/pages/datasets/uccs/index.md +++ b/site/content/pages/datasets/uccs/index.md @@ -20,43 +20,37 @@ authors: Adam Harvey ### sidebar ### end sidebar -UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications"[^uccs_vast]. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge".[^funding_uccs] In this investigation, we examine the contents of the dataset, funding sources, photo EXIF data, and information from publicly available research project citations. - +UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications"[^uccs_vast]. According to the authors of [two](https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745) [papers](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1) associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge".[^funding_uccs] In this investigation, we examine the contents of the [dataset](http://vast.uccs.edu/Opensetface/), its funding sources, photo EXIF data, and information from publicly available research project citations. The UCCS dataset includes over 1,700 unique identities, most of which are students walking to and from class. As of 2018, it was the "largest surveillance [face recognition] benchmark in the public domain."[^surv_face_qmul] The photos were taken during the spring semesters of 2012 – 2013 on the West Lawn of the University of Colorado Colorado Springs campus. The photographs were timed to capture students during breaks between their scheduled classes in the morning and afternoon during Monday through Thursday. "For example, a student taking Monday-Wednesday classes at 12:30 PM will show up in the camera on almost every Monday and Wednesday."[^sapkota_boult]. -![caption: Example images from the UnConstrained College Students Dataset. ](assets/uccs_grid.jpg) +![caption: The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps](assets/uccs_map_aerial.jpg) -The long-range surveillance images in the UnContsrained College Students dataset were captured using a Canon 7D 18 megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured."[^sapkota_boult] -Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face detection and recognition systems for real world applications in defense, intelligence, and commercial applications. -![caption: The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps](assets/uccs_map_aerial.jpg) +The long-range surveillance images in the UnContsrained College Students dataset were taken using a Canon 7D 18-megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured."[^sapkota_boult] +Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face recognition systems for real world applications for defense, intelligence, and commercial partners. -In the two papers associated with the release of the UCCS dataset ([Unconstrained Face Detection and Open-Set Face Recognition Challenge](https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745) and [Large Scale Unconstrained Open Set Face Database](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1)), the researchers disclosed their funding sources as ODNI (United States Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. Further, UCCS's VAST site explicity [states](https://vast.uccs.edu/project/iarpa-janus/) they are part of the [IARPA Janus](https://www.iarpa.gov/index.php/research-programs/janus), a face recognition project developed to serve the needs of national intelligence interests. +![caption: Example images from the UnConstrained College Students Dataset. ](assets/uccs_grid.jpg) -The EXIF data embedded in the images shows that the photo capture times follow a similar pattern, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken on Friday shows that the researchers were only interested in capturing images of students. +The EXIF data embedded in the images shows that the photo capture times follow a similar pattern to that outlined by the researchers, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken between Friday through Sunday shows that the researchers were only interested in capturing images of students during the peak campus hours. ![caption: UCCS photos captured per weekday © megapixels.cc](assets/uccs_exif_plot_days.png) ![caption: UCCS photos captured per weekday © megapixels.cc](assets/uccs_exif_plot.png) -The two research papers associated with the release of the UCCS dataset ([Unconstrained Face Detection and Open-Set Face Recognition Challenge](https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745) and [Large Scale Unconstrained Open Set Face Database](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1)), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), Small Business Innovation Research (SBIR), Special Operations Command and Small Business Innovation Research (SOCOM SBIR), and the National Science Foundation. Further, UCCS's VAST site explicitly [states](https://vast.uccs.edu/project/iarpa-janus/) they are part of the [IARPA Janus](https://www.iarpa.gov/index.php/research-programs/janus), a face recognition project developed to serve the needs of national intelligence interests, clearly establishing the the funding sources and immediate benefactors of this dataset are United States defense and intelligence agencies. - - -Although the images were first captured in 2012 – 2013 the dataset was not publicly released until 2016. Then in 2017 the UCCS face dataset formed the basis for a defense and intelligence agency funded [face recognition challenge](http://www.face-recognition-challenge.com/) project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the [2nd Unconstrained Face Detection and Open Set Recognition Challenge](https://erodner.github.io/ial2018eccv/) at the European Computer Vision Conference (ECCV) in Munich, Germany. - -As of April 15, 2019, the UCCS dataset is no longer available for public download. But during the three years it was publicly available (2016-2019) the UCCS dataset appeared in at least 6 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military. +The two research papers associated with the release of the UCCS dataset ([Unconstrained Face Detection and Open-Set Face Recognition Challenge](https://www.semanticscholar.org/paper/Unconstrained-Face-Detection-and-Open-Set-Face-G%C3%BCnther-Hu/d4f1eb008eb80595bcfdac368e23ae9754e1e745) and [Large Scale Unconstrained Open Set Face Database](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1)), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContsrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), and the Special Operations Command and Small Business Innovation Research (SOCOM SBIR) amongst others. UCCS's VAST site also explicitly [states](https://vast.uccs.edu/project/iarpa-janus/) their involvement in the [IARPA Janus](https://www.iarpa.gov/index.php/research-programs/janus) face recognition project developed to serve the needs of national intelligence, establishing that immediate benefactors of this dataset include United States defense and intelligence agencies, but it would go on to benefit other similar organizations. +In 2017, one year after its public release, the UCCS face dataset formed the basis for a defense and intelligence agency funded [face recognition challenge](http://www.face-recognition-challenge.com/) project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the [2nd Unconstrained Face Detection and Open Set Recognition Challenge](https://erodner.github.io/ial2018eccv/) at the European Computer Vision Conference (ECCV) in Munich, Germany. +As of April 15, 2019, the UCCS dataset is no longer available for public download. But during the three years it was publicly available (2016-2019) the UCCS dataset appeared in at least 6 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military; and Vision Semantics Ltd who lists the UK Ministory of Defence as a project partner. {% include 'dashboard.html' %} {% include 'supplementary_header.html' %} - -To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions. +Since this site To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions. ![caption: GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018](assets/uccs_pgan_01.jpg) @@ -98,7 +92,7 @@ To show the types of face images used in the UCCS student dataset while protecti ### Location -The location of the camera and subjects can confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The [original papers](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1) also provides another clue: a [picture of the camera](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1/figure/1) inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the [location on Google Maps](https://www.google.com/maps/place/University+of+Colorado+Colorado+Springs/@38.8934297,-104.7992445,27a,35y,258.51h,75.06t/data=!3m1!1e3!4m5!3m4!1s0x87134fa088fe399d:0x92cadf3962c058c4!8m2!3d38.8968312!4d-104.8049528) +The location of the camera and subjects was confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The [original papers](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1) also provides another clue: a [picture of the camera](https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1/figure/1) inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the [location on Google Maps](https://www.google.com/maps/place/University+of+Colorado+Colorado+Springs/@38.8934297,-104.7992445,27a,35y,258.51h,75.06t/data=!3m1!1e3!4m5!3m4!1s0x87134fa088fe399d:0x92cadf3962c058c4!8m2!3d38.8968312!4d-104.8049528) ![caption: 3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps](assets/uccs_map_3d.jpg) diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 7b965bd4..bd4fb8d9 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -61,30 +61,30 @@ -SenseNets, SenseTime -Attention-Aware Compositional Network for Person Re-identification -SemanticScholar +Beihang University +Orientation-Guided Similarity Learning for Person Re-identification +ieee.org 2018 ✔ -SenseTime -End-to-End Deep Kronecker-Product Matching for Person Re-identification -thcvf.com +Beihang University +Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology +acm.org 2018 ✔ CloudWalk -Horizontal Pyramid Matching for Person Re-identification -arxiv.org -2018 +CloudWalk re-identification technology extends facial biometric tracking with improved accuracy +BiometricUpdate.com +2019 ✔ -Megvii -Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project -SemanticScholar +CloudWalk +Horizontal Pyramid Matching for Person Re-identification +arxiv.org 2018 ✔ @@ -97,15 +97,15 @@ Megvii -SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial PersonRe-Identification -arxiv.org +Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project +SemanticScholar 2018 ✔ -CloudWalk -CloudWalk re-identification technology extends facial biometric tracking with improved accuracy -BiometricUpdate.com +Megvii +SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial PersonRe-Identification +arxiv.org 2018 ✔ @@ -124,16 +124,16 @@ ✔ -Beihang University -Orientation-Guided Similarity Learning for Person Re-identification -ieee.org +SenseNets, SenseTime +Attention-Aware Compositional Network for Person Re-identification +SemanticScholar 2018 ✔ -Beihang University -Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology -acm.org +SenseTime +End-to-End Deep Kronecker-Product Matching for Person Re-identification +thcvf.com 2018 ✔ @@ -180,19 +180,19 @@ ✔ -Vision Semantics Ltd. -Unsupervised Person Re-identification by Deep Learning Tracklet Association -arxiv.org -2018 -✔ - - US Dept. of Homeland Security Re-Identification with Consistent Attentive Siamese Networks arxiv.org 2019 ✔ + +Vision Semantics Ltd. +Unsupervised Person Re-identification by Deep Learning Tracklet Association +arxiv.org +2018 +✔ +
By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original intent of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking".
@@ -260,7 +260,7 @@
Supplementary Information
Video Timestamps
-
The video timestamps contain the likely, but not yet confirmed, date and times the video recorded. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least confirms the relative timing. The [precipitous weather on March 14, 2014 in Durham, North Carolina supports, but does not confirm, that this day is a potential capture date.
+
The video timestamps contain the likely, but not yet confirmed, date and times the video recorded. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least confirms the relative timing. The precipitous weather on March 14, 2014 in Durham, North Carolina supports, but does not confirm, that this day is a potential capture date.

@@ -332,9 +332,9 @@

Camera

Notes
-
-
The original Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812, and their own research typically mentions 2,000. For this write up we used 2,000 to describe the approximate number of students.
-
+
The original Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812, and their own research typically mentions 2,000. For this write up we used 2,000 to describe the approximate number of students.
+
Ethics
+
Please direct any questions about the ethics of the dataset to Duke University's Institutional Ethics & Compliance Office using the number at the bottom of the page.

Cite Our Work
@@ -348,7 +348,7 @@ title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets}, year = 2019, url = {https://megapixels.cc/}, - urldate = {2019-04-20} + urldate = {2019-04-18} }
@@ -360,11 +360,7 @@ booktitle = {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking}, year = {2016} } -
ToDo
-
-
clean up citations, formatting
-
-
References
a
Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html. April 14, 2019.
+
References
a
Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html. April 14, 2019.

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/

a
https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/

a
"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. SemanticScholar
diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html index 9347d536..3652e329 100644 --- a/site/public/datasets/uccs/index.html +++ b/site/public/datasets/uccs/index.html @@ -49,14 +49,14 @@

Website

uccs.edu
-
UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications" 1. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge". 3 In this investigation, we examine the contents of the dataset, funding sources, photo EXIF data, and information from publicly available research project citations.
+
UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs developed primarily for research and development of "face detection and recognition research towards surveillance applications" 1. According to the authors of two papers associated with the dataset, over 1,700 students and pedestrians were "photographed using a long-range high-resolution surveillance camera without their knowledge". 3 In this investigation, we examine the contents of the dataset, its funding sources, photo EXIF data, and information from publicly available research project citations.

The UCCS dataset includes over 1,700 unique identities, most of which are students walking to and from class. As of 2018, it was the "largest surveillance [face recognition] benchmark in the public domain." 4 The photos were taken during the spring semesters of 2012 – 2013 on the West Lawn of the University of Colorado Colorado Springs campus. The photographs were timed to capture students during breaks between their scheduled classes in the morning and afternoon during Monday through Thursday. "For example, a student taking Monday-Wednesday classes at 12:30 PM will show up in the camera on almost every Monday and Wednesday." 2.
-
Example images from the UnConstrained College Students Dataset.
The long-range surveillance images in the UnContsrained College Students dataset were taken using a Canon 7D 18-megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured." 2 -Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face recognition systems for real world applications in defense, intelligence, and commercial sectors.
-
The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps
The EXIF data embedded in the images shows that the photo capture times follow a similar pattern, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken on Friday shows that the researchers were only interested in capturing images of students.
-
UCCS photos captured per weekday © megapixels.cc
UCCS photos captured per weekday © megapixels.cc
The two research papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContsrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), and the Special Operations Command and Small Business Innovation Research (SOCOM SBIR) amongst others. UCCS's VAST site also explicitly states that they are part of the IARPA Janus, a face recognition project developed to serve the needs of national intelligence interests, clearly establishing the the funding sources and immediate benefactors of this dataset are United States defense and intelligence agencies.
-
Although the images were first captured in 2012 – 2013 the dataset was not publicly released until 2016. In 2017 the UCCS face dataset formed the basis for a defense and intelligence agency funded face recognition challenge project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the 2nd Unconstrained Face Detection and Open Set Recognition Challenge at the European Computer Vision Conference (ECCV) in Munich, Germany.
-
As of April 15, 2019, the UCCS dataset is no longer available for public download. But during the three years it was publicly available (2016-2019) the UCCS dataset appeared in at least 6 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military.
+
The location at University of Colorado Colorado Springs where students were surreptitiously photographed with a long-range surveillance camera for use in a defense and intelligence agency funded research project on face recognition. Image: Google Maps
The long-range surveillance images in the UnContsrained College Students dataset were taken using a Canon 7D 18-megapixel digital camera fitted with a Sigma 800mm F5.6 EX APO DG HSM telephoto lens and pointed out an office window across the university's West Lawn. The students were photographed from a distance of approximately 150 meters through an office window. "The camera [was] programmed to start capturing images at specific time intervals between classes to maximize the number of faces being captured." 2 +Their setup made it impossible for students to know they were being photographed, providing the researchers with realistic surveillance images to help build face recognition systems for real world applications for defense, intelligence, and commercial partners.
+
Example images from the UnConstrained College Students Dataset.
The EXIF data embedded in the images shows that the photo capture times follow a similar pattern to that outlined by the researchers, but also highlights that the vast majority of photos (over 7,000) were taken on Tuesdays around noon during students' lunch break. The lack of any photos taken between Friday through Sunday shows that the researchers were only interested in capturing images of students during the peak campus hours.
+
UCCS photos captured per weekday © megapixels.cc
UCCS photos captured per weekday © megapixels.cc
The two research papers associated with the release of the UCCS dataset (Unconstrained Face Detection and Open-Set Face Recognition Challenge and Large Scale Unconstrained Open Set Face Database), acknowledge that the primary funding sources for their work were United States defense and intelligence agencies. Specifically, development of the UnContsrianed College Students dataset was funded by the Intelligence Advanced Research Projects Activity (IARPA), Office of Director of National Intelligence (ODNI), Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative (ONR MURI), and the Special Operations Command and Small Business Innovation Research (SOCOM SBIR) amongst others. UCCS's VAST site also explicitly states their involvement in the IARPA Janus face recognition project developed to serve the needs of national intelligence, establishing that immediate benefactors of this dataset include United States defense and intelligence agencies, but it would go on to benefit other similar organizations.
+
In 2017, one year after its public release, the UCCS face dataset formed the basis for a defense and intelligence agency funded face recognition challenge project at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was again used for the 2nd Unconstrained Face Detection and Open Set Recognition Challenge at the European Computer Vision Conference (ECCV) in Munich, Germany.
+
As of April 15, 2019, the UCCS dataset is no longer available for public download. But during the three years it was publicly available (2016-2019) the UCCS dataset appeared in at least 6 publicly available research papers including verified usage from Beihang University who is known to provide research and development for China's military; and Vision Semantics Ltd who lists the UK Ministory of Defence as a project partner.

Who used UCCS?
@@ -117,7 +117,7 @@ Their setup made it impossible for students to know they were being photographed
Supplementary Information
-
To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions.
+
Since this site To show the types of face images used in the UCCS student dataset while protecting their individual privacy, a generative adversarial network was used to interpolate between identities in the dataset. The image below shows a generative adversarial network trained on the UCCS face bounding box areas from 16,000 images and over 90,000 face regions.

GAN generated approximations of students in the UCCS dataset. © megapixels.cc 2018
UCCS photos taken in 2012
@@ -211,7 +211,7 @@ Their setup made it impossible for students to know they were being photographed

Location
-
The location of the camera and subjects can confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The original papers also provides another clue: a picture of the camera inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the location on Google Maps
+
The location of the camera and subjects was confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The original papers also provides another clue: a picture of the camera inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the location on Google Maps

3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps
Funding

The UnConstrained College Students dataset is associated with two main research papers: "Large Scale Unconstrained Open Set Face Database" and "Unconstrained Face Detection and Open-Set Face Recognition Challenge". Collectively, these papers and the creation of the dataset have received funding from the following organizations:
@@ -246,7 +246,7 @@ Their setup made it impossible for students to know they were being photographed title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets}, year = 2019, url = {https://megapixels.cc/}, - urldate = {2019-04-20} + urldate = {2019-04-18} }
-- cgit v1.2.3-70-g09d2 From 699d7a77b9d4120dfb75f271cb924b0e05a2fcaa Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Wed, 17 Apr 2019 20:02:42 +0200 Subject: basic mobile css --- client/map/index.js | 9 ++ megapixels/app/site/parser.py | 3 +- site/assets/css/css.css | 143 +++++++++++++++++++++---- site/content/pages/datasets/duke_mtmc/index.md | 23 ++-- site/includes/cite_our_work.html | 2 +- site/public/datasets/duke_mtmc/index.html | 53 ++++----- 6 files changed, 171 insertions(+), 62 deletions(-) (limited to 'site/public/datasets/duke_mtmc') diff --git a/client/map/index.js b/client/map/index.js index 475ba3c6..29fc2286 100644 --- a/client/map/index.js +++ b/client/map/index.js @@ -150,6 +150,15 @@ export default function append(el, payload) { el.removeChild(mapCover) } }) + mapCover.querySelector('div').addEventListener('touchstart', (e) => { + e.preventDefault() + }) + mapCover.querySelector('div').addEventListener('tap', () => { + map.scrollWheelZoom.enable() + if (mapCover.parentNode === el) { + el.removeChild(mapCover) + } + }) function stopPropagation(e) { e.stopPropagation() } diff --git a/megapixels/app/site/parser.py b/megapixels/app/site/parser.py index 1489d056..92d950f8 100644 --- a/megapixels/app/site/parser.py +++ b/megapixels/app/site/parser.py @@ -260,7 +260,8 @@ def format_footnotes(footnotes, s3_path): continue key, note = footnote.split(': ', 1) footnote_index_lookup[key] = index - footnote_list.append('{}_BACKLINKS'.format(key, key) + markdown(note)) + note_markup = markdown(note).replace('
', '', 1).replace('
', '', 1) + footnote_list.append('{} {}_BACKLINKS'.format(index, key, key) + note_markup) index += 1 footnote_txt = '
' + '
'.join(footnote_list) + '
' diff --git a/site/assets/css/css.css b/site/assets/css/css.css index 492ec347..a5e36542 100644 --- a/site/assets/css/css.css +++ b/site/assets/css/css.css @@ -55,6 +55,7 @@ header .logo { margin-right: 8px; width: 20px; height: 20px; + flex: 0 0 20px; } header .site_name { font-family: 'Roboto', sans-serif; @@ -142,7 +143,7 @@ footer { color: #888; font-size: 9pt; line-height: 17px; - padding: 20px 0 20px; + padding: 20px 20px; font-family: "Roboto", sans-serif; } footer > div { @@ -161,7 +162,7 @@ footer a { transition: color 0.1s cubic-bezier(0,0,1,1); margin-right: 5px; } -footer a:hover { +.desktop footer a:hover { color: #ddd; } @@ -318,7 +319,7 @@ p.subp{ } .right-sidebar { float: right; - width: 240px; + width: 200px; margin: 0px 20px 20px 20px; padding-top: 12px; padding-left: 20px; @@ -359,10 +360,11 @@ p.subp{ text-decoration: none; border-bottom: 1px solid; } -.left-sidebar a, .right-sidebar a{ +.left-sidebar a, .right-sidebar a { border-bottom: 1px solid #666; } -.content .left-sidebar a:hover, .content .right-sidebar a:hover{ +.desktop .content .left-sidebar a:hover, +.desktop .content .right-sidebar a:hover { border-bottom: 1px solid #ccc; } @@ -389,7 +391,7 @@ code { background: rgba(255,255,255,0.1); } pre { - margin: 0 auto; + margin: 0 auto 20px auto; max-width: 720px; border: 1px solid #666; border-radius: 2px; @@ -455,8 +457,8 @@ blockquote { } /* Buttons */ -.citation-opts { +.citation-opts { } .citation-opts li{ display: inline-block; @@ -475,22 +477,24 @@ blockquote { background: #444; border:0px; } + /* footnotes */ + .footnotes hr { display: none; } -ul.footnotes{ - max-width:720px; - margin:0 auto; - font-size:12px; +ul.footnotes { + max-width: 720px; + margin: 0 auto; + font-size: 12px; } -ul.footnotes li{ - font-size:12px; - list-style-type: decimal; - margin-bottom:12px; +ul.footnotes li { + font-size: 12px; + list-style-type: none; + margin-bottom: 12px; } -ul.footnotes p{ - font-size:12px; +ul.footnotes p { + font-size: 12px; } .footnotes ol:before { content: 'Footnotes'; @@ -575,12 +579,12 @@ section.fullwidth .image { margin: 10px auto 10px auto; font-family: 'Roboto'; } -.caption a{ - color:#ccc; +.caption a { + color: #ccc; border: 0; } -.caption a:hover{ - color:#fff; +.desktop .caption a:hover { + color: #fff; border: 0; } @@ -751,7 +755,7 @@ section.fullwidth .image { .desktop .content .dataset-list a { border: none; } -.dataset-list a:hover{ +.desktop .dataset-list a:hover { border: none; } .dataset-list a:nth-child(3n+1) { background-color: rgba(255, 0, 0, 0.1); } @@ -1087,6 +1091,7 @@ a.footnote_shim { .desktop a.footnote:hover { /*background-color: #ff8;*/ color: #fff; + border: 0; } .backlinks { margin-right: 10px; @@ -1101,6 +1106,9 @@ a.footnote_shim { bottom: 5px; margin-right: 2px; } +.content .footnotes .backlinks a { + bottom: 3px; +} li p { margin: 0; padding: 0; display: inline; @@ -1113,7 +1121,7 @@ li p { margin: 10px auto; padding-bottom: 10px } -.download-btn{ +.download-btn { display: inline-block; font-size: 13px; color: #ddd; @@ -1122,4 +1130,93 @@ li p { padding: 8px 10px; border-radius: 5px; transition: all 0.1s; +} + +/* iphone/ipad css */ +@media all and (max-device-width: 1024px) { + /* header / footer */ + .slogan { + padding-left: 10px; + } + header .splash { + display: none; + } + header .links a { + margin-right: 10px; + } + + /* content */ + + .intro_section { + padding: 50px 0 20px 0; + } + .intro_section .hero_desc { + font-size: 28px; + line-height: 50px; + margin-bottom: 20px; + } + .intro_section .inner { + margin: 0; + max-width: 100%; + padding: 20px; + } + .intro_section .hero_subdesc { + max-width: 100%; + } + section h1, section h2, section h3, section h4, section h5, section h6, section p { + max-width: 100%; + } + section { + width: 100%; + padding: 0 10px; + } + .meta { + margin-right: 0px; + margin-bottom: 10px; + } + .modal img { + max-width: 100%; + } + th { + overflow: hidden; + text-overflow: ellipsis; + } + .citationHeader { + + } + section.wide { + width: 100%; + } + .map, .map .applet { + height: 360px; + } +} + +/* iphone-specific */ +@media all and (max-device-width: 640px) { + .right-sidebar { + float: none; + width: 100%; + border: 0; + margin: 0; + padding: 0 2px; + border-bottom: 1px solid #333; + } + .map, .map .applet { + height: 360px; + } + .citationBrowser input.q { + max-width: 180px; + } + .columns { + flex-direction: column; + } + .columns .column { + margin: 0; + } + .columns-2 .column, + .columns-3 .column, + .columns-4 .column { + width: 100%; + } } \ No newline at end of file diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md index dd4551d9..2140fed7 100644 --- a/site/content/pages/datasets/duke_mtmc/index.md +++ b/site/content/pages/datasets/duke_mtmc/index.md @@ -43,7 +43,7 @@ Despite [repeated](https://www.hrw.org/news/2017/11/19/china-police-big-data-sys | Beihang University | Orientation-Guided Similarity Learning for Person Re-identification | [ieee.org](https://ieeexplore.ieee.org/document/8545620) | 2018 | ✔ | | Beihang University | Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology | [acm.org](https://dl.acm.org/citation.cfm?id=3240663) | 2018 | ✔ | -The reasons that companies in China use the Duke MTMC dataset for research are technically no different than the reasons it is used in the United States and Europe. In fact the original creators of the dataset published a follow up report in 2017 titled [Tracking Social Groups Within and Across Cameras](https://www.semanticscholar.org/paper/Tracking-Social-Groups-Within-and-Across-Cameras-Solera-Calderara/9e644b1e33dd9367be167eb9d832174004840400) with specific applications to "automated analysis of crowds and social gatherings for surveillance and security applications". Their work, as well as the creation of the original dataset in 2014 were both supported in part by the United States Army Research Laboratory. +The reasons that companies in China use the Duke MTMC dataset for research are technically no different than the reasons it is used in the United States and Europe. In fact, the original creators of the dataset published a follow up report in 2017 titled [Tracking Social Groups Within and Across Cameras](https://www.semanticscholar.org/paper/Tracking-Social-Groups-Within-and-Across-Cameras-Solera-Calderara/9e644b1e33dd9367be167eb9d832174004840400) with specific applications to "automated analysis of crowds and social gatherings for surveillance and security applications". Their work, as well as the creation of the original dataset in 2014 were both supported in part by the United States Army Research Laboratory. Citations from the United States and Europe show a similar trend to that in China, including publicly acknowledged and verified usage of the Duke MTMC dataset supported or carried out by the United States Department of Homeland Security, IARPA, IBM, Microsoft (who provides surveillance to ICE), and Vision Semantics (who works with the UK Ministry of Defence). One [paper](https://pdfs.semanticscholar.org/59f3/57015054bab43fb8cbfd3f3dbf17b1d1f881.pdf) is even jointly published by researchers affiliated with both the University College of London and the National University of Defense Technology in China. @@ -59,14 +59,14 @@ Citations from the United States and Europe show a similar trend to that in Chin By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original [intent](http://vision.cs.duke.edu/DukeMTMC/) of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking". -The same logic applies for all the new extensions of the Duke MTMC dataset including [Duke MTMC Re-ID](https://github.com/layumi/DukeMTMC-reID_evaluation), [Duke MTMC Video Re-ID](https://github.com/Yu-Wu/DukeMTMC-VideoReID), Duke MTMC Groups, and [Duke MTMC Attribute](https://github.com/vana77/DukeMTMC-attribute). And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called [QMUL-SurvFace](https://qmul-survface.github.io/), which was funded in part by [SeeQuestor](https://seequestor.com), a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, security contractors, and defense agencies using these datasets to advance their organization's work, Duke MTMC provides significant value regardless of who else is using it so long as it accelerate advances their own interests in artificial intelligence. +The same logic applies for all the new extensions of the Duke MTMC dataset including [Duke MTMC Re-ID](https://github.com/layumi/DukeMTMC-reID_evaluation), [Duke MTMC Video Re-ID](https://github.com/Yu-Wu/DukeMTMC-VideoReID), Duke MTMC Groups, and [Duke MTMC Attribute](https://github.com/vana77/DukeMTMC-attribute). And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called [QMUL-SurvFace](https://qmul-survface.github.io/), which was funded in part by [SeeQuestor](https://seequestor.com), a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, security contractors, and defense agencies using these datasets to advance their organization's work, Duke MTMC provides significant value regardless of who else is using it, so long as it advances their own interests in artificial intelligence. ![caption: Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_saliencies.jpg) -But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against universal human rights, or against your own political interests. +But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against your own political interests, or against universal human rights. -For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies. +For the approximately 2,000 students in Duke MTMC dataset, there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies. ![caption: Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc](assets/duke_mtmc_cameras.jpg) @@ -80,7 +80,7 @@ For the approximately 2,000 students in Duke MTMC dataset there is unfortunately #### Video Timestamps -The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop [time sync data](http://vision.cs.duke.edu/DukeMTMC/details.html#time-sync) provided by the researchers, it at least aligns the relative time. The [rainy weather](https://www.wunderground.com/history/daily/KIGX/date/2014-3-19?req_city=Durham&req_state=NC&req_statename=North%20Carolina&reqdb.zip=27708&reqdb.magic=1&reqdb.wmo=99999) on that day also contribute towards the likelihood of March 14, 2014.. +The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop [time sync data](http://vision.cs.duke.edu/DukeMTMC/details.html#time-sync) provided by the researchers, it at least aligns the relative time. The [rainy weather](https://www.wunderground.com/history/daily/KIGX/date/2014-3-19?req_city=Durham&req_state=NC&req_statename=North%20Carolina&reqdb.zip=27708&reqdb.magic=1&reqdb.wmo=99999) on that day also contributes towards the likelihood of March 14, 2014. === columns 2 @@ -103,11 +103,13 @@ The video timestamps contain the likely, but not yet confirmed, date and times o === end columns -#### Notes +#### Errata -- The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812 +- The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812. -If you use any data from the Duke MTMC please follow their [license](http://vision.cs.duke.edu/DukeMTMC/#how-to-cite) and cite their work as: +#### Citing Duke MTMC + +If you use any data from the Duke MTMC, please follow their [license](http://vision.cs.duke.edu/DukeMTMC/#how-to-cite) and cite their work as:
@inproceedings{ristani2016MTMC, @@ -120,14 +122,13 @@ If you use any data from the Duke MTMC please follow their [license](http://visi {% include 'cite_our_work.html' %} - #### ToDo - clean up citations, formatting ### Footnotes -[^xinjiang_nyt]: Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html. April 14, 2019. +[^duke_mtmc_orig]: "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. [SemanticScholar](https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c) [^sensetime_qz]: [^sensenets_uyghurs]: -[^duke_mtmc_orig]: "Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. [SemanticScholar](https://www.semanticscholar.org/paper/Performance-Measures-and-a-Data-Set-for-Tracking-Ristani-Solera/27a2fad58dd8727e280f97036e0d2bc55ef5424c) \ No newline at end of file +[^xinjiang_nyt]: Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html. April 14, 2019. diff --git a/site/includes/cite_our_work.html b/site/includes/cite_our_work.html index 810561e7..8625355e 100644 --- a/site/includes/cite_our_work.html +++ b/site/includes/cite_our_work.html @@ -11,7 +11,7 @@ title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets}, year = 2019, url = {https://megapixels.cc/}, - urldate = {2019-04-20} + urldate = {2019-04-18} }

diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index def7b8fa..86cccd11 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -46,9 +46,9 @@

Website

duke.edu
-
Duke MTMC (Multi-Target, Multi-Camera) is a dataset of surveillance video footage taken on Duke University's campus in 2014 and is used for research and development of video tracking systems, person re-identification, and low-resolution facial recognition. The dataset contains over 14 hours of synchronized surveillance video from 8 cameras at 1080p and 60FPS with over 2 million frames of 2,000 students walking to and from classes. The 8 surveillance cameras deployed on campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy" 4.
+
Duke MTMC (Multi-Target, Multi-Camera) is a dataset of surveillance video footage taken on Duke University's campus in 2014 and is used for research and development of video tracking systems, person re-identification, and low-resolution facial recognition. The dataset contains over 14 hours of synchronized surveillance video from 8 cameras at 1080p and 60FPS with over 2 million frames of 2,000 students walking to and from classes. The 8 surveillance cameras deployed on campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy" 1.

In this investigation into the Duke MTMC dataset we tracked down over 100 publicly available research papers that explicitly acknowledged using Duke MTMC. Our analysis shows that the dataset has spread far beyond its origins and intentions in academic research projects at Duke University. Since its publication in 2016, more than twice as many research citations originated in China as in the United States. Among these citations were papers with explicit and direct links to the Chinese military and several of the companies known to provide Chinese authorities with the oppressive surveillance technology used to monitor millions of Uighur Muslims.
-
In one 2018 paper jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled Attention-Aware Compositional Network for Person Re-identification, the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. 2 3 1
+
In one 2018 paper jointly published by researchers from SenseNets and SenseTime (and funded by SenseTime Group Limited) entitled Attention-Aware Compositional Network for Person Re-identification, the Duke MTMC dataset was used for "extensive experiments" on improving person re-identification across multiple surveillance cameras with important applications in "finding missing elderly and children, and suspect tracking, etc." Both SenseNets and SenseTime have been directly linked to the providing surveillance technology to monitor Uighur Muslims in China. 2 3 4
A collection of 1,600 out of the approximately 2,000 students and pedestrians in the Duke MTMC dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification, and eventually the QMUL SurvFace face recognition dataset. Open Data Commons Attribution License.
Despite repeated warnings by Human Rights Watch that the authoritarian surveillance used in China represents a violation of human rights, researchers at Duke University continued to provide open access to their dataset for anyone to use for any project. As the surveillance crisis in China grew, so did the number of citations with links to organizations complicit in the crisis. In 2018 alone there were over 70 research projects happening in China that publicly acknowledged benefiting from the Duke MTMC dataset. Amongst these were projects from SenseNets, SenseTime, CloudWalk, Megvii, Beihang University, and the PLA's National University of Defense Technology.
@@ -146,7 +146,7 @@

-
The reasons that companies in China use the Duke MTMC dataset for research are technically no different than the reasons it is used in the United States and Europe. In fact the original creators of the dataset published a follow up report in 2017 titled Tracking Social Groups Within and Across Cameras with specific applications to "automated analysis of crowds and social gatherings for surveillance and security applications". Their work, as well as the creation of the original dataset in 2014 were both supported in part by the United States Army Research Laboratory.
+
The reasons that companies in China use the Duke MTMC dataset for research are technically no different than the reasons it is used in the United States and Europe. In fact, the original creators of the dataset published a follow up report in 2017 titled Tracking Social Groups Within and Across Cameras with specific applications to "automated analysis of crowds and social gatherings for surveillance and security applications". Their work, as well as the creation of the original dataset in 2014 were both supported in part by the United States Army Research Laboratory.

Citations from the United States and Europe show a similar trend to that in China, including publicly acknowledged and verified usage of the Duke MTMC dataset supported or carried out by the United States Department of Homeland Security, IARPA, IBM, Microsoft (who provides surveillance to ICE), and Vision Semantics (who works with the UK Ministry of Defence). One paper is even jointly published by researchers affiliated with both the University College of London and the National University of Defense Technology in China.
@@ -203,9 +203,9 @@

By some metrics the dataset is considered a huge success. It is regarded as highly influential research and has contributed to hundreds, if not thousands, of projects to advance artificial intelligence for person tracking and monitoring. All the above citations, regardless of which country is using it, align perfectly with the original intent of the Duke MTMC dataset: "to accelerate advances in multi-target multi-camera tracking".
-
The same logic applies for all the new extensions of the Duke MTMC dataset including Duke MTMC Re-ID, Duke MTMC Video Re-ID, Duke MTMC Groups, and Duke MTMC Attribute. And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called QMUL-SurvFace, which was funded in part by SeeQuestor, a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, security contractors, and defense agencies using these datasets to advance their organization's work, Duke MTMC provides significant value regardless of who else is using it so long as it accelerate advances their own interests in artificial intelligence.
-
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc
But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against universal human rights, or against your own political interests.
-
For the approximately 2,000 students in Duke MTMC dataset there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies.
+
The same logic applies for all the new extensions of the Duke MTMC dataset including Duke MTMC Re-ID, Duke MTMC Video Re-ID, Duke MTMC Groups, and Duke MTMC Attribute. And it also applies to all the new specialized datasets that will be created from Duke MTMC, such as the low-resolution face recognition dataset called QMUL-SurvFace, which was funded in part by SeeQuestor, a computer vision provider to law enforcement agencies including Scotland Yards and Queensland Police. From the perspective of academic researchers, security contractors, and defense agencies using these datasets to advance their organization's work, Duke MTMC provides significant value regardless of who else is using it, so long as it advances their own interests in artificial intelligence.
+
Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc
But this perspective comes at significant cost to civil rights, human rights, and privacy. The creation and distribution of the Duke MTMC illustrates an egregious prioritization of surveillance technologies over individual rights, where the simple act of going to class could implicate your biometric data in a surveillance training dataset, perhaps even used by foreign defense agencies against your own ethics, against your own political interests, or against universal human rights.
+
For the approximately 2,000 students in Duke MTMC dataset, there is unfortunately no escape. It would be impossible to remove oneself from all copies of the dataset downloaded around the world. Instead, over 2,000 students and visitors who happened to be walking to class on March 13, 2014 will forever remain in all downloaded copies of the Duke MTMC dataset and all its extensions, contributing to a global supply chain of data that powers governmental and commercial expansion of biometric surveillance technologies.

Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc
Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.

Who used Duke MTMC Dataset?
@@ -267,7 +267,7 @@
Supplementary Information

Video Timestamps
-
The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least aligns the relative time. The rainy weather on that day also contribute towards the likelihood of March 14, 2014..
+
The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least aligns the relative time. The rainy weather on that day also contributes towards the likelihood of March 14, 2014.

@@ -338,11 +338,12 @@

Camera

-
Notes
+
Errata

-
The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812
+
The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812.

-
If you use any data from the Duke MTMC please follow their license and cite their work as:
+
Citing Duke MTMC
+
If you use any data from the Duke MTMC, please follow their license and cite their work as:
 @inproceedings{ristani2016MTMC,
  title =        {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
@@ -363,7 +364,7 @@
   title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
   year = 2019,
   url = {https://megapixels.cc/},
-  urldate = {2019-04-20}
+  urldate = {2019-04-18}
 }
@@ -371,25 +372,25 @@

clean up citations, formatting

-
References
a
Mozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html. April 14, 2019.
-
a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
-
a
https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/
-
a
"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. SemanticScholar
+
References
1 a"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. SemanticScholar +
2 ahttps://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/ +
3 ahttps://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/ +
4 aMozur, Paul. "One Month, 500,000 Face Scans: How China Is Using A.I. to Profile a Minority". https://www.nytimes.com/2019/04/14/technology/china-surveillance-artificial-intelligence-racial-profiling.html. April 14, 2019.

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