  Accurate data association is crucial in reducing confusion, such as ID
switches and assignment errors, in multi-object tracking (MOT). However,
existing advanced methods often overlook the diversity among trajectories and
the ambiguity and conflicts present in motion and appearance cues, leading to
confusion among detections, trajectories, and associations when performing
simple global data association. To address this issue, we propose a simple,
versatile, and highly interpretable data association approach called Decomposed
Data Association (DDA). DDA decomposes the traditional association problem into
multiple sub-problems using a series of non-learning-based modules and
selectively addresses the confusion in each sub-problem by incorporating
targeted exploitation of new cues. Additionally, we introduce Occlusion-aware
Non-Maximum Suppression (ONMS) to retain more occluded detections, thereby
increasing opportunities for association with trajectories and indirectly
reducing the confusion caused by missed detections. Finally, based on DDA and
ONMS, we design a powerful multi-object tracker named DeconfuseTrack,
specifically focused on resolving confusion in MOT. Extensive experiments
conducted on the MOT17 and MOT20 datasets demonstrate that our proposed DDA and
ONMS significantly enhance the performance of several popular trackers.
Moreover, DeconfuseTrack achieves state-of-the-art performance on the MOT17 and
MOT20 test sets, significantly outperforms the baseline tracker ByteTrack in
metrics such as HOTA, IDF1, AssA. This validates that our tracking design
effectively reduces confusion caused by simple global association.
