DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion

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 more MOT algorithms that rely less on visual discrimination and depend more on motion analysis. We benchmark several state-of-the-art trackers on our dataset and observe a significant performance drop on DanceTrack when compared against existing benchmarks. The dataset, project code and competition server are released at: \url{https://github.com/DanceTrack}.