In this paper, we propose MOTRv2, a simple yet effective pipeline to
bootstrap end-to-end multi-object tracking with a pretrained object detector.
Existing end-to-end methods, MOTR and TrackFormer are inferior to their
tracking-by-detection counterparts mainly due to their poor detection
performance. We aim to improve MOTR by elegantly incorporating an extra object
detector. We first adopt the anchor formulation of queries and then use an
extra object detector to generate proposals as anchors, providing detection
prior to MOTR. The simple modification greatly eases the conflict between joint
learning detection and association tasks in MOTR. MOTRv2 keeps the query
propogation feature and scales well on large-scale benchmarks. MOTRv2 ranks the
1st place (73.4% HOTA on DanceTrack) in the 1st Multiple People Tracking in
Group Dance Challenge. Moreover, MOTRv2 reaches state-of-the-art performance on
the BDD100K dataset. We hope this simple and effective pipeline can provide
some new insights to the end-to-end MOT community. Code is available at
https://github.com/megvii-research/MOTRv2.

本论文提出 MOTRv2, 旨在通过一个预先训练好的物体定位器，启动端到端的多目标跟踪。利用额外的物体探测器来提高 MOTR 的性能，并排名第一，最终达到了先进的性能。