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------------
status: published
title: Duke MTMC
desc: <span class="dataset-name">Duke MTMC</span> is a dataset of surveillance camera footage of students on Duke University campus
subdesc: 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
slug: duke_mtmc
cssclass: dataset
image: assets/background.jpg
published: 2019-2-23
updated: 2019-2-23
authors: Adam Harvey
------------
## Duke MTMC
### sidebar
### end sidebar
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].
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]
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.
| 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 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.
| 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.
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' %}
#### 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 |
===========
| 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
#### 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](http://vision.cs.duke.edu/DukeMTMC/#how-to-cite) and cite their work as:
<pre>
@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}
}
</pre>
{% 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]: <https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/>
[^sensenets_uyghurs]: <https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/>
[^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)
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