Multi-object Tracking Method Based on Efficient Channel Attention and Switchable Atrous Convolution

In recent years,object detection and data association have getting remarkable progress which are the core components for multi-object tracking. In multi-object tracking field,the main strategy is tracking-by-detection. Although the detection based tracking method can get great results, it is relies on the performance of the detector. In complex scene, detector can not provide reliable results. Moreover,due to the incorrect detection results, data association process can not be trusted. Based on this motivation, this paper focuses on improving the accuracy of detection and data association. We introduce the efficient channel attention module to the backbone network, which can adaptively extract important information in images. Furthermore, we apply switchable atrous convolution in the network to dynamically adjust the receptive field according to object changes. In data association process, the appearance features with minimum occlusion are saved for each existing trajectory, which are used for re-associate after the objects are lost. Extensive experiments on MOT16,MOT17 and MOT20 challenging datasets demonstrate that our method is comparable with the state-of-the-art multi-object tracking methods.