Effective Hierarchical Data Association in Multi-Object Tracking Using an Enhanced Network Flow Framework

In the realm of multi-object tracking, the SORT model is esteemed for its simplicity and efficiency, yet its tracking efficacy hinges significantly on detector performance. Randomly discarding low-threshold detections can result in critical misses and track fragmentation. Hence, it is often complemented by a hierarchical data association strategy centered on detection thresholds. Nonetheless, relying solely on threshold-based categorization may segregate detections and lead to redundant detections for the same target, thereby increasing the impact of redundancies on tracking and causing trajectory drift. To address this, this paper introduces a novel hierarchical data association framework based on network flow, integrating historical trajectory and domain information to efficiently group detections and redundancies. Additionally, an effective global topological structure graph is proposed to manage redundant detections. Experimental results demonstrate competitive performance on MOTChallenge, with a 1-2\% improvement in MOTA over the benchmark, particularly nearing 2% on MOT20.