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.
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本文探讨了多目标跟踪中的 tracking-by-detection 模式，结合运动模型与数据驱动技术，使标准的 re-identification 网络在外观跟踪方面取得了极佳的结果，并在四个公共数据集上实现了最先进的表现。