PFF-CB: Multiscale Occlusion Pedestrian Detection Method Based on PFF and CBAM

Occlusion pedestrian detection is an important and difficult task in pedestrian detection. At present, the main method to deal with occlusion pedestrian detection usually adopts pedestrian parts or human body relationship methods. However, in the scene of crowd occlusion or severe pedestrian occlusion, only small parts of the body can be used for detection. Pedestrian parts or human body relationship methods cannot effectively address these issues. In view of the above problems, this paper abandoned the occlusion processing method of pedestrian parts or human body relationship. Considering that it is difficult to establish the relationship between parts and key points. The scale of visible parts of the occlusion pedestrian is small, and the scale of no occlusion pedestrian and occlusion pedestrian in the same picture is different. A multiscale feature attention fusion network named parallel feature fusion with CBAM (PFF-CB) is proposed for occlusion pedestrian detection. Feature information of different scales can be integrated effectively in the PFF-CB module. PFF-CB module uses a convolutional block attention module (CBAM) to enhance the important feature information in space and channel. A parallel feature fusion module based on FPN is used to enhance key features. The performance of the proposed module was tested on two common data sets of occlusion pedestrians with different occlusion types. The results show that the PFF-CB module makes a good performance in occlusion pedestrian detection tasks.