Multi-Object Tracking (MOT) aims to detect and associate all desired objects
across frames. Most methods accomplish the task by explicitly or implicitly
leveraging strong cues (i.e., spatial and appearance information), which
exhibit powerful instance-level discrimination. However, when object occlusion
and clustering occur, both spatial and appearance information will become
ambiguous simultaneously due to the high overlap between objects. In this
paper, we demonstrate that this long-standing challenge in MOT can be
efficiently and effectively resolved by incorporating weak cues to compensate
for strong cues. Along with velocity direction, we introduce the confidence
state and height state as potential weak cues. With superior performance, our
method still maintains Simple, Online and Real-Time (SORT) characteristics.
Furthermore, our method shows strong generalization for diverse trackers and
scenarios in a plug-and-play and training-free manner. Significant and
consistent improvements are observed when applying our method to 5 different
representative trackers. Further, by leveraging both strong and weak cues, our
method Hybrid-SORT achieves superior performance on diverse benchmarks,
including MOT17, MOT20, and especially DanceTrack where interaction and
occlusion are frequent and severe. The code and models are available at
this https URL

多目标跟踪 (MOT) 任务可以通过添加弱线索来解决目标遮挡和聚类问题，并在多个追踪器上展现出了明显和持续的改进。