Spatio-Temporal Correlation Learning for Multiple Object Tracking

Abstract

Multi-object tracking (MOT) has gained remarkable progress in recent years, while due to the complexity of real-world environments, there are still many challenges that remain unsolved, such as object occlusion and deformation. To effectively alleviate this problem, we propose a simple yet effective Transformer-based tracker, named CLNet, consisting of an Instance-Aware Localization (IAL) module and a Temporal Context Aggregation (TCA) module. Specifically, the former learns the correlation of object positions for potential location estimation, and the latter learns the correlation of background contexts to obtain robust re-ID features for data association. Experimental results show that CLNet outperforms the baseline method by +2.2 MOTA and +2.3 IDF1 on MOT17 and +6.3 MOTA and +3.6 IDF1 on MOT20 respectively, which demonstrate the effectiveness of the proposed method.