论文标题
MOTRV2：通过验证对象检测器进行端到端多目标跟踪
MOTRv2: Bootstrapping End-to-End Multi-Object Tracking by Pretrained Object Detectors
论文作者
论文摘要
在本文中，我们提出了MOTRV2，这是一种简单而有效的管道，用于使用预审前的对象检测器进行端到端的多目标跟踪。现有的端到端方法，MOTR和TrackFormer不如它们的跟踪，主要是由于它们的检测性能不佳。我们旨在通过优雅地合并额外的对象检测器来改善MOTR。我们首先采用查询的锚定公式，然后使用额外的对象检测器将提案作为锚定，并在MOTR之前提供检测。简单的修改极大地减轻了MOTR中联合学习检测与关联任务之间的冲突。 MOTRV2可以保持查询促进功能，并在大规模的基准测试上很好地缩放。 MOTRV2在集体舞蹈挑战赛中的第一个人追踪中排名第一（Dancetrack上的73.4％HOTA）。此外，MOTRV2在BDD100K数据集上达到最先进的性能。我们希望这种简单有效的管道可以为端到端的MOT社区提供一些新的见解。代码可在\ url {https://github.com/megvii-research/motrv2}中找到。
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 \url{https://github.com/megvii-research/MOTRv2}.
