From 43c3e3904f80eb56769fba4634729d0e567f9a32 Mon Sep 17 00:00:00 2001 From: adamhrv Date: Wed, 17 Apr 2019 16:51:44 +0200 Subject: update duke --- site/public/datasets/duke_mtmc/index.html | 231 ++++++++++++++++++++++++------ 1 file changed, 187 insertions(+), 44 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 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
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[ page under development ]

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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.

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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.

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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.

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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.

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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.
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.

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

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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.

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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

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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.
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.

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OrganizationPaperLinkYearUsed Duke MTMC
SenseNets, SenseTimeAttention-Aware Compositional Network for Person Re-identificationSemanticScholar2018✔
SenseTimeEnd-to-End Deep Kronecker-Product Matching for Person Re-identificationthcvf.com2018✔
CloudWalkHorizontal Pyramid Matching for Person Re-identificationarxiv.org20xx✔
MegviiMulti-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC ProjectSemanticScholar2018✔
MegviiPerson Re-Identification (slides)github.io2017✔
MegviiSCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial PersonRe-Identificationarxiv.org2018✔
CloudWalkCloudWalk re-identification technology extends facial biometric tracking with improved accuracyBiometricUpdate.com2018✔
CloudWalkHorizontal Pyramid Matching for Person Re-identificationarxiv.org]2018✔
National University of Defense TechnologyTracking by Animation: Unsupervised Learning of Multi-Object Attentive TrackersSemanticScholar.org2018✔
National University of Defense TechnologyUnsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention NetworksSemanticScholar.org2018✔
Beihang UniversityOrientation-Guided Similarity Learning for Person Re-identificationieee.org2018✔
Beihang UniversityOnline Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topologyacm.org2018✔
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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.

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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.

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OrganizationPaperLinkYearUsed Duke MTMC
IARPA, IBM, CloudWalkHorizontal Pyramid Matching for Person Re-identificationarxiv.org2018✔
MicrosoftReXCam: Resource-Efficient, Cross-CameraVideo Analytics at Enterprise Scalearxiv.org2018✔
MicrosoftScaling Video Analytics Systems to Large Camera Deploymentsarxiv.org2018✔
University College of London, National University of Defense TechnologyUnsupervised Multi-Object Detection for Video Surveillance Using Memory-Based RecurrentAttention NetworksPDF2018✔
Vision Semantics Ltd.Unsupervised Person Re-identification by Deep Learning Tracklet Associationarxiv.org2018✔
US Dept. of Homeland SecurityRe-Identification with Consistent Attentive Siamese Networksarxiv.org2019✔
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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".

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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.

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

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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.

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Who used Duke MTMC Dataset?

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Supplementary Information

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Funding

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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.

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Video Timestamps

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

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Opting Out

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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.

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Notes

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Notes

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

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+@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}
+}
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Cite Our Work

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}

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

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-@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}
-}
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ToDo

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ToDo

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References

References

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