PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues
This work addresses occlusion robustness in multi-object tracking for applications in consumer electronics, representing an incremental advancement over existing methods.
The paper tackles the problem of multi-object tracking performance degradation under heavy occlusions by incorporating pseudo-depth cues into the tracking-by-detection paradigm, achieving leading performances on benchmarks like DanceTrack, MOT17, and MOT20 with significant improvements in occlusion-heavy scenarios.
Multi-object tracking (MOT) is a rising topic in video processing technologies and has important application value in consumer electronics. Currently, tracking-by-detection (TBD) is the dominant paradigm for MOT, which performs target detection and association frame by frame. However, the association performance of TBD methods degrades in complex scenes with heavy occlusions, which hinders the application of such methods in real-world scenarios.To this end, we incorporate pseudo-depth cues to enhance the association performance and propose Pseudo-Depth SORT (PD-SORT). First, we extend the Kalman filter state vector with pseudo-depth states. Second, we introduce a novel depth volume IoU (DVIoU) by combining the conventional 2D IoU with pseudo-depth. Furthermore, we develop a quantized pseudo-depth measurement (QPDM) strategy for more robust data association. Besides, we also integrate camera motion compensation (CMC) to handle dynamic camera situations. With the above designs, PD-SORT significantly alleviates the occlusion-induced ambiguous associations and achieves leading performances on DanceTrack, MOT17, and MOT20. Note that the improvement is especially obvious on DanceTrack, where objects show complex motions, similar appearances, and frequent occlusions. The code is available at https://github.com/Wangyc2000/PD_SORT.