DfTrack: Deconfused Data Association Framework for Multi-Object Tracking

Accurate data association plays a crucial role in Multi-Object Tracking (MOT) as it helps reduce confusion such as identity switches and assignment errors. However, many existing advanced methods often overlook the diversity among trajectories and the ambiguity and conflicts present in various types of cues. Consequently, when performing simple global data association, confusion arises between detections, trajectories, and associations. To address this problem, we propose a simple, versatile, and highly interpretable Deconfused Data Association Framework (DDAF). DDAF decomposes the traditional association problem into multiple sub-problems using a series of non-learnable modules, and selectively resolves confusion in each sub-problem by strategically utilizing new cues. Building upon DDAF, we design a powerful multi-object tracker named DfTrack, which specifically targets confusion in MOT. Furthermore, we discuss different specific implementations of DDAF to tackle challenging environments characterized by low frame rate, camera motion, and cross-domain scenarios. Correspondingly, we also develop several variants of DfTrack, demonstrating the remarkable scalability and adaptability of DDAF. Extensive experiments conducted on the MOT17, MOT20, and DanceTrack datasets demonstrate that DDAF significantly outperforms simple global association methods, and its variants can adapt to various challenging environments. Furthermore, DfTrack achieves state-of-the-art performance on multiple datasets, with HOTA of 65.2%, 63.9%, and 64.4% on MOT17, MOT20, and DanceTrack, respectively. The DfTrack-Hybrid variant further improves the performance on this basis. These results validate that our DDAF can effectively decompose and resolve various confusion in global association without any learning cost.