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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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This bar chart presents a ranking of the top countries where citations originated. Mouse over individual columns to see yearly totals. These charts show only the top 10 countries overall.
These pie charts show overall totals based on country and institution type.
Citations were collected from Semantic Scholar, a website which aggregates and indexes research papers. The citations were 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 train and/or test machine learning algorithms.
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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
People involved: Ergys Ristani, Francesco Solera, Roger S. Zou, Rita Cucchiara, Carlo Tomasi.
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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]