**作者：**Maggiolino, Gerard, Adnan Ahmad, Jinkun Cao, and Kris Kitani
作者单位：卡内基·梅隆大学
发布时间：2023
发布期刊/会议：arxiv
出版商：
论文全称：DEEP OC-SORT: MULTI-PEDESTRIAN TRACKING BY ADAPTIVE RE-IDENTIFICATION
论文地址：
论文代码：
https://github.com/GerardMaggiolino/Deep-OC-SORT
地位：
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.