Occluded Pedestrian Detection Algorithm Based on Improved Network Structure of YOLOv3
LIU Li1,2, ZHENG Yang1,2,3, FU Dongmei1,2
1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083 2. Beijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology Beijing, Beijing 100083 3. Shunde Graduate School, University of Science and Technology Beijing, Foshan 528399
Abstract Aiming at high missed detection rates of YOLOv3 for occluded pedestrian in surveillance video, a detection method for occluded pedestrian based on improved network structure of YOLOv3 is proposed. Firstly, the spatial pyramid pooling network is introduced into the fully connected layer to enhance the multi-scale feature fusion capability of the network. Secondly, the network structure pruning is employed to eliminate the network structure redundancy to avoid network degeneration and overfitting problem caused by the deepening of network layers and reduce the amount of parameters. Multi-scale training is performed on the corridor pedestrian dataset to obtain the best weight model. Experimental results indicate the improvement of average accuracy and detection speed of the proposed algorithm.
Fund:Fundamental Research Funds for the Central Universities of University of Science and Technology Beijing(No.FRF-BD-19-002A)
Corresponding Authors:
LIU Li, Ph.D., professor. Her research interests include computer network and pattern recognition.
About author:: ZHENG Yang, master student. His research interests include deep learning, target detection and tracking. FU Dongmei, Ph.D., professor. Her research interests include deep learning and pa-ttern recognition.
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2020, Vol. 33 
