For a long time, the most common paradigm in Multi-Object Tracking was tracking-by-detection (TbD), where objects are first detected and then associated over video frames. For association, most models resourced to motion and appearance cues, e.g., re-identification networks. Recent approaches based on attention propose to learn the cues in a data-driven manner, showing impressive results. In this paper, we ask ourselves whether simple good old TbD methods are also capable of achieving the performance of end-to-end models. To this end, we propose two key ingredients that allow a standard re-identification network to excel at appearance-based tracking. We extensively analyse its failure cases, and show that a combination of our appearance features with a simple motion model leads to strong tracking results. Our tracker generalizes to four public datasets, namely MOT17, MOT20, BDD100k, and DanceTrack, achieving state-of-the-art performance. We will release the code and models

翻译：长期以来，多目标跟踪中最主流的范式是“先检测后跟踪”（TbD），即先检测目标，再在视频帧之间进行关联。在关联阶段，大多数模型依赖运动与外观线索（例如重识别网络）。近年来，基于注意力机制的方法提出以数据驱动方式学习线索，展现出令人瞩目的结果。本文探讨一个核心问题：传统的简单TbD方法是否也能达到端到端模型的性能？为此，我们提出两个关键要素，使标准重识别网络在外观跟踪中表现卓越。我们深入分析了其失败案例，并证明将外观特征与简单运动模型相结合可产生强大的跟踪效果。我们的跟踪器在MOT17、MOT20、BDD100k和DanceTrack四个公开数据集上均实现泛化，并达到当前最优性能。我们将公开代码与模型。