Multi-Object Tracking Via Multi-Attention
Xianrui Wang, Hefei Ling, Jiazhong Chen, Ping Li · 2020
Data association plays a crucial role in Multi-Object Tracking(MOT), but it is usually suppressed by occlusion. In this paper, we propose an online MOT approach via multiple attention mechanism(Multi-Attention) to handle the frequent interactions between targets. Specifically, the proposed Multi-Attention consists of spatial-attention, channel-attention, and temporal-attention three modules. The spatial-attention module lets the network focus on visible local areas by generating a visibility map, and the channel-attention module combines texture information and context information adaptively to build a recognizable object descriptor, then the temporal-attention module pays different attention to objects in the same trajectory avoiding the suppress caused by contaminated samples. Besides, a multiple branch convolutional block called receptive filed module(RFModule) is introduced to learn multiple levels of information for Multi-Attention. The experimental results on MOTChallenging benchmarks demonstrate the effectiveness of the proposed MOT algorithm against both online and offline trackers.