An Adaptive Attention-Aware Method for Occluded Multi-Pedestrian Tracking

Occlusion has always been a difficulty in multi-pedestrian detection and tracking. The foreground object occlusion and the overlapping self-occlusion bring many problems of false detection and identity loss. In this paper, we propose an adaptive attention-aware method for occluding pedestrian tracking, which belongs to detection-based online tracking. To deal with the person re-identification under short-term occlusion, we define a global-local occlusion coefficient and segment the occluded part of pedestrians by spatial-temporal attention module, so that the discriminative features can be adaptively extracted. During data association, the trajectory matching cost matrix is set to adjust the target pedestrian matching. Experimental results demonstrate the superior performance of our method in videos with severe occlusion, indicating its effectiveness for tracking occluded pedestrians.