Abstract
Pedestrian detection in occluded scenes has always been a thorny problem in computer vision. In this case, due to the large difference in scale of occluded pedestrians and low visibility, it usually brings great challenges to detection. To solve this problem, this paper proposes a model structure for pedestrian occlusion detection, which improves the pedestrian detection method based on anchor-free. Specifically, we introduce a structure for extracting multi-scale context information to learn a better feature representation, and a channel attention module on each decoder layer to provide global context as a guidance of low-level features to select category localization details. Experimental results show that this method achieves 41.93% of MR-2 on the occlusion subset of Caltech pedestrian dataset, which is better than other contrast detectors.
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