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