Abstract
Motion-based association for Multi-Object Tracking (MOT) has recently re-achieved prominence with the rise of powerful object detectors. Despite this, little work has been done to incorporate appearance cues beyond simple heuristic models that lack robustness to feature degradation. In this paper, we propose a novel way to leverage objects' appearances to adaptively integrate appearance matching into existing high-performance motion-based methods. Building upon the pure motion-based method OC-SORT, we achieve 1st place on MOT20 and 2nd place on MOT17 with 63.9 and 64.9 HOTA, respectively. We also achieve 61.3 HOTA on the challenging DanceTrack benchmark as a new state-of-the-art even compared to more heavily-designed methods. The code and models are available at \url{this https URL}.
Abstract (translated)
基于运动的关系型多目标跟踪(MOT)最近随着 powerful 对象检测器的崛起而重新获得了关注。尽管如此,还没有大量的工作涉及到将外观线索引入到简单启发模型之外,这些模型缺乏特征退化的鲁棒性。在本文中,我们提出了一种新方法,利用物体的外观来自适应地将其集成到现有的高性能运动型方法中。基于纯粹的运动型方法OC-SORT,我们在MOT20和MOT17比赛中分别获得了第一和第二名,分别获得了63.9和64.9HOTA。我们还在具有挑战性的DanceTrack基准测试中获得了61.3HOTA,成为最新的前沿技术,即使与更加精心设计的方法相比也是如此。代码和模型可在 url{this https URL} 上获取。
URL
https://arxiv.org/abs/2302.11813
