Recently, Multi-Object Tracking (MOT) has attracted rising attention, and
accordingly, remarkable progresses have been achieved. However, the existing
methods tend to use various basic models (e.g, detector and embedding model),
and different training or inference tricks, etc. As a result, the construction
of a good baseline for a fair comparison is essential. In this paper, a classic
tracker, i.e., DeepSORT, is first revisited, and then is significantly improved
from multiple perspectives such as object detection, feature embedding, and
trajectory association. The proposed tracker, named StrongSORT, contributes a
strong and fair baseline for the MOT community. Moreover, two lightweight and
plug-and-play algorithms are proposed to address two inherent "missing"
problems of MOT: missing association and missing detection. Specifically,
unlike most methods, which associate short tracklets into complete trajectories
at high computation complexity, we propose an appearance-free link model
(AFLink) to perform global association without appearance information, and
achieve a good balance between speed and accuracy. Furthermore, we propose a
Gaussian-smoothed interpolation (GSI) based on Gaussian process regression to
relieve the missing detection. AFLink and GSI can be easily plugged into
various trackers with a negligible extra computational cost (1.7 ms and 7.1 ms
per image, respectively, on MOT17). Finally, by fusing StrongSORT with AFLink
and GSI, the final tracker (StrongSORT++) achieves state-of-the-art results on
multiple public benchmarks, i.e., MOT17, MOT20, DanceTrack and KITTI. Codes are
available at this https URL and
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这篇研究提出了 StrongSORT++ 的算法，结合了经典的 DeepSORT 追踪器及多方面的改进，并使用两种轻量级的插件算法 AFLink 和 GSI 解决 MOT 中的缺失关联和缺失检测问题，从而在多个公共基准测试中实现了最新的技术进展。