Fusion detection and ReID embedding with hybrid attention for multi-object tracking

Multi-object tracking (MOT) involves the prediction of object identities and their corresponding bounding boxes within video or image sequences. While numerous models have been proposed for MOT, there is still a lack of discrimination of object features and severe ID switches during the tracking stage. This paper presents a novel fusion detection and re-identification (ReID) embedding with hybrid attention for multi-object tracking to address this issue. It incorporates two major cores: a hybrid attention module (HAM) and an embedding association module (EAM). Firstly, the HAM comprises spatial-aware attention, scale-aware attention, and task-aware attention, aiming to obtain more informative features. By integrating these mechanisms, the proposed model can effectively handle variations in object scales and spatial relationships to promote discrimination and balance two tasks (detection and ReID). Secondly, we introduce an embedding association module to address the unreliable similarity matching during the tracking. Specifically, the EAM not only considers the appearance similarity but also ponders on geometric attributes to improve the ability to track in the presence of object occlusions and brief disappearances. Extensive experiments are conducted on the public MOT Challenge datasets, demonstrating that our method performs better than other advanced methods.