  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
https://github.com/dyhBUPT/HIT.
