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Duke MTMC is a dataset of CCTV footage of students at Duke University

Duke MTMC contains over 2 million video frames and 2,000 unique identities collected from 8 cameras at Duke University campus in March 2014 +

Duke Multi-Target, Multi-Camera Tracking Dataset (Duke MTMC)

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Information Supply Chain

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+ To understand how Duke MTMC Dataset has been used around the world... + affected global research on computer vision, surveillance, defense, and consumer technology, the and where this dataset has been used the locations of each organization that used or referenced the datast +

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+ Data is compiled from Semantic Scholar and not yet manually verified. +

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Supplementary Information

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Citations

+ Citations were collected from Semantic Scholar, a website which aggregates + and indexes research papers. Metadata was extracted from these papers, including extracting names of institutions automatically from PDFs, and then the addresses were geocoded. Data is not yet manually verified, and reflects anytime the paper was cited. Some papers may only mention the dataset in passing, while others use it as part of their research methodology. +

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Research Notes

"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:
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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
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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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