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------------
status: published
title: Duke Multi-Target, Multi-Camera Tracking
desc: <span class="dataset-name">Duke MTMC</span> is a dataset of CCTV footage of students at Duke University
subdesc: Duke MTMC contains over 2 million video frames and 2,000 unique identities collected from 8 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
------------
### sidebar
+ Collected: March 19, 2014
+ Cameras: 8
+ Video Frames: 2,000,000
+ Identities: Over 2,000
+ Used for: Person re-identification, <br>face recognition
+ Sector: Academic
+ Website: <a href="http://vision.cs.duke.edu/DukeMTMC/">duke.edu</a>
## Duke Multi-Target, Multi-Camera Tracking Dataset (Duke MTMC)
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## Research Notes
- "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
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]
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