A Novel Scene-aware Pedestrian Detection in Dense Scenes
Ting Chen, Jinghua Chen, Tao Gao, Shukang Zhu, Zongyang Guo, Zixiang Liu, Ziqi Li, Quanzhao Zhao · 2024
In crowded scenes, detecting pedestrians with high density and various occlusions is always an important yet challenging task. To further improve the performance of one-stage detectors in detecting crowded pedestrians, we propose a one-stage dense pedestrian detection network called YOLO- DensePed (You Only Look Once-Dense Pedestrian Detection) to further overcome shortcoming including limited receptive field, insufficient feature fusion, and ambiguous assignment of anchor boxes for object detection. First, the proposed YOLO-DensePed utilizes a multi-head self-attention module with embedded Gaussian masks to reduce background redundant information, as well as enhance capture capability for global contextual information; Then, Deformable ConvNets v2 (DCNv2) are used instead of standard convolutions in the neck layer, which can dynamically adjust the receptive field and learn the correct feature and multi-scale position information of objects; Furthermore, SimOTA dynamic sample assignment strategy and Soft-NMS post-processing algorithm are also introduced to assist the YOLO-DensePed for better handling occlusion and dense distribution issues. Extensive experiments on the public CrowdHuman dataset demonstrate that YOLO-DensePed consistently presents the best or comparable performance, allowing for efficient and accurate detection of crowded pedestrian.