Towards Occlusion-Aware Multi-Pedestrian Tracking
Hechuang Wang, Tong Chen, Yifan Wang · Applied Sciences · 2025
Achieving robust multi-object tracking in complex real-world scenarios remains a challenging task. Existing approaches often struggle to effectively handle occlusion, primarily because occlusion can result in unreliable appearance features, inaccurate motion estimation, and biased association cues. To address these challenges, this study proposes OATrack, a pedestrian multi-object tracking framework with explicit occlusion awareness. First, an occlusion perception module is introduced to estimate the occlusion rate and provide it as input for subsequent components. Subsequently, the Kalman Filter’s innovation gain is adaptively suppressed according to the target’s occlusion level, and association cues are assigned adaptive weights based on occlusion severity. Experimental results on the MOT17 benchmark dataset demonstrate that the proposed method achieves state-of-the-art performance in key tracking metrics. Specifically, on the MOT17 test set, the method achieves an IDF1 score of 80.6% and a HOTA score of 65.3%. On the MOT20 test set, it attains an IDF1 of 77.8% and a HOTA of 63.6%. The proposed algorithm offers an effective solution for multi-object tracking in environments characterized by frequent and complex occlusions.