Brainwash Dataset
+Brainwash is a dataset of livecam images taken from San Francisco's Brainwash Cafe. It includes 11,918 images of "everyday life of a busy downtown cafe" 1 captured at 100 second intervals throught the entire day. The Brainwash dataset includes 3 full days of webcam images taken on October 27, November 13, and November 24 in 2014. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com 2
+The Brainwash dataset is unique because it uses images from a publicly available webcam that records people inside a privately owned business without any consent. No ordinary cafe custom could ever suspect there image would end up in dataset used for surveillance reserach and development, but that is exactly what happened to customers at Brainwash cafe in San Francisco.
+Although Brainwash appears to be a less popular dataset, it was used in 2016 and 2017 by researchers from the National University of Defense Technology in China took note of the dataset and used it for two research projects on advancing the capabilities of object detection to more accurately isolate the target region in an image (PDF). 3 4. The dataset also appears in a 2017 research paper from Peking University for the purpose of improving surveillance capabilities for "people detection in the crowded scenes".
+
Who used IJB-C?
+ ++ 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 IJB-C has been used around the world by commercial, military, and academic organizations; existing publicly available research citing IARPA Janus Benchmark C 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 +
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. If you use our data, please cite our work. +
+ + +Supplementary Information
+ +
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 = {2019-04-18}
+}
+
+
+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.
+
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.
+
References
- 1 a"readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385. +
- 2 aStewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016. +
- 3 aLi, 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. +
- 4 aZhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.
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.
-<<<<<<< HEAD -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
-======= -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
->>>>>>> 61fbcb8f2709236f36a103a73e0bd9d1dd3723e8 +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. 4 2 3
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.
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".
-<<<<<<< HEADThe 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.
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.
-======= -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.
-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 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.
->>>>>>> 61fbcb8f2709236f36a103a73e0bd9d1dd3723e8
Who used Duke MTMC Dataset?
@@ -270,11 +260,7 @@Supplementary Information
Video Timestamps
-<<<<<<< HEAD -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.
-=======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.
->>>>>>> 61fbcb8f2709236f36a103a73e0bd9d1dd3723e8| Camera | @@ -345,28 +331,11 @@
|---|
Errata
--
-
- The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812. -
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},
- 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}
-}
-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 writeup 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
@@ -383,17 +352,8 @@ }
-<<<<<<< HEAD -ToDo
--
-
- clean up citations, formatting -
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. -======= -
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},
@@ -401,26 +361,25 @@
booktitle = {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking},
year = {2016}
}
-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 -
- 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
->>>>>>> 61fbcb8f2709236f36a103a73e0bd9d1dd3723e8 +
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.