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

Sixian Chan, Chenhao Qiu, Dijuan Wu, Jie Hu, Ali Asghar Heidari, Huiling Chen

Abstract: Highlights•We present a fusion detection and ReID embedding mechanism for MOT. Extensive experiments illustrate our model achieves competitive performance.•We design a HAM module to extract discriminative features for better performance in subsequent detection and ReID tasks.•We introduce an EAM module to handle the unreliable similarity matching during tracking.