DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion
Peize Sun, Jinkun Cao, Yi Jiang, Zehuan Yuan, Song Bai, Kris Kitani,, Ping Luo

TL;DR
DanceTrack introduces a large-scale dataset emphasizing motion analysis over appearance for multi-object tracking, challenging existing models and fostering development of more robust algorithms in scenarios with similar appearances.
Contribution
The paper presents DanceTrack, a new dataset focusing on humans with similar appearance and diverse motion, and benchmarks existing trackers to highlight their limitations in such scenarios.
Findings
State-of-the-art trackers perform poorly on DanceTrack
Existing models rely heavily on appearance cues
Motion analysis is crucial for challenging tracking scenarios
Abstract
A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association. This pipeline is partially motivated by recent progress in both object detection and re-ID, and partially motivated by biases in existing tracking datasets, where most objects tend to have distinguishing appearance and re-ID models are sufficient for establishing associations. In response to such bias, we would like to re-emphasize that methods for multi-object tracking should also work when object appearance is not sufficiently discriminative. To this end, we propose a large-scale dataset for multi-human tracking, where humans have similar appearance, diverse motion and extreme articulation. As the dataset contains mostly group dancing videos, we name it "DanceTrack". We expect DanceTrack to provide a better platform to develop…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Face recognition and analysis · Human Pose and Action Recognition
MethodsTrack objects as points
