Multi-Object Tracking (MOT) aims to detect and associate all targets of given
classes across frames. Current dominant solutions, e.g. ByteTrack and
StrongSORT++, follow the hybrid pipeline, which first accomplish most of the
associations in an online manner, and then refine the results using offline
tricks such as interpolation and global link. While this paradigm offers
flexibility in application, the disjoint design between the two stages results
in suboptimal performance. In this paper, we propose the Hierarchical IoU
Tracking framework, dubbed HIT, which achieves unified hierarchical tracking by
utilizing tracklet intervals as priors. To ensure the conciseness, only IoU is
utilized for association, while discarding the heavy appearance models, tricky
auxiliary cues, and learning-based association modules. We further identify
three inconsistency issues regarding target size, camera movement and
hierarchical cues, and design corresponding solutions to guarantee the
reliability of associations. Though its simplicity, our method achieves
promising performance on four datasets, i.e., MOT17, KITTI, DanceTrack and
VisDrone, providing a strong baseline for future tracking method design.
Moreover, we experiment on seven trackers and prove that HIT can be seamlessly
integrated with other solutions, whether they are motion-based,
appearance-based or learning-based. Our codes will be released at
this https URL

本论文提出了一种名为 HIT 的分层 IoU 跟踪框架，通过利用 tracklet 间隔作为先验，实现统一的分层跟踪，并利用 IoU 进行关联，舍弃了繁重的外观模型、复杂的辅助线索和基于学习的关联模块，解决了关于目标尺寸、摄像机运动和分层线索的三个不一致问题，并在四个数据集上取得了良好的性能，为未来的跟踪方法设计提供了强大的基线，并证明了 HIT 可以与其他解决方案无缝集成。