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Duke Imagelab 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

DukeMTMC Downloads

Dataset Extensions

Below is a list of dataset extensions provided by the community:

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-devkitused for evaluation by MOTChallenge. For 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. The details of our measures appear in our paper.

Tracking Systems

We provide code for the following tracking systems which are all based on Correlation Clustering optimization:

Below is a list of extensions to our code provided by the community:


[1] Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. E. Ristani, F. Solera, R. S. Zou, R. Cucchiara and C. Tomasi. ECCV 2016 Workshop on Benchmarking Multi-Target Tracking. [pdf]

[2] Tracking Social Groups Within and Across Cameras. F. Solera, S. Calderara, E. Ristani, C. Tomasi, and R. Cucchiara. IEEE Transactions on Circuits and Systems 2016. [pdf]

[3] Tracking Multiple People Online and in Real Time. E. Ristani and C. Tomasi. ACCV 2014. [pdf]

How to Cite

If you use our work, please cite our papers accordingly:

Data Set, Performance Measures, Multi-Camera Tracking System [Bibtex]
Single-Camera Tracking System [Bibtex]
People-Groups Tracking System [Bibtex]

If you use or extend our data, please see the license terms.

Support or Contact

Having trouble with the data or code? Please contact Ergys Ristani or Francesco Solera. We will help you sort it out.


1 Solera, Francesco; Calderara, Simone; Ristani, Ergys; Tomasi, Carlo; Cucchiara, Rita "Tracking social groups within and across cameras" IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, vol. 27, pp. 441 -453 , 2017 | DOI: 10.1109/TCSVT.2016.2607378 Journal

Research Activity Info