Adj-MOT: Multi-object Tracking by ReID with Adjacent Frame Enhancement

Tianyang Dong, Shuqian Lv, Guoqing Zhao, Wenyuan Ying, Chengkai Tong

Abstract: A ReID-based multi-object tracking (MOT) network with adjacent frame enhancement is proposed, which is constructed with center-based detection network. To enrich the ReID features, our method treats the adjacent frame as data augmentation of the current frame, implicitly uses the network to achieve data alignment for each object at different frames, and then uses the aligned adjacent features to enhance the ReID. In the training phase, the method adds a location prediction branch to guide the network to learn the feature correspondence of the same object in two frames. In the inference phase, our method uses historical tracking to construct a heatmap of interest of the adjacent frame to inform the network of the locations of all objects in adjacent frames, then the network autonomously mines the features of each object in these two frames. Our approach performs better in MOT challenges than the existing multi-object tracking network.