DanceTrack

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

Peize Sun* Jinkun Cao* Yi Jiang Zehuan Yuan Song Bai Kris Kitani Ping Luo

The University of Hong Kong Carnegie Mellon University ByteDance Inc.

*: equal contribution

[Arxiv preprint] [Codalab Competition] [Github]

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 detec- tion and re-ID, and partially motivated by biases in existing tracking datasets, where most objects tend to have distin- guishing appearance and re-ID models are sufficient for es- tablishing associations. In response to such bias, we would like to re-emphasize that methods for multi-object track- ing should also work when object appearance is not suffi- ciently discriminative. To this end, we propose a large-scale dataset for multi-human tracking, where humans have sim- ilar 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.

Benchmark Results