  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, spatial and appearance information will become ambiguous
simultaneously due to the high overlap among objects. In this paper, we
demonstrate 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 and height state as
potential weak cues. With superior performance, our method still maintains
Simple, Online and Real-Time (SORT) characteristics. Also, 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, with both
strong and weak cues, our method Hybrid-SORT achieves superior performance on
diverse benchmarks, including MOT17, MOT20, and especially DanceTrack where
interaction and severe occlusion frequently happen with complex motions. The
code and models are available at https://github.com/ymzis69/HybridSORT.
