Learning Correlation for Online Multiple Object Tracking

Abstract

Existing multiple object tracking methods usually strengthen data association by discriminative identity embeddings. However, many works treat object detection and association as two individual tasks, thus gaining limited benefits. In this paper, we follow the joint detection and tracking paradigm to learn correlation for online multiple object tracking. The proposed method, named LCTrack, links the two tasks by an attention mechanism. Specifically, for robust feature representations, we introduce an identity-aware attention module to extract reliable identity embeddings and model their correlation between two consecutive frames. Furthermore, for effective correlation learning, we design a target-aware loss to train the identity embedding extraction, which is well compatible with the detection task. Therefore, LCTrack can boost the position prediction and data association by the enhanced feature representation. Experimental results on the MOTChallenge benchmarks demonstrate the effectiveness and favorable performance of the proposed LCTrack in comparison with state-of-the-art methods.