Video abnormal action detection based on enhanced video Swin transformer
Anheng Xie, Longye Wang · 2023
A recognition method based on the enhanced Transformer model is proposed to solve the task of human abnormal action recognition in surveillance videos. Video Swin Transformer (VST) is used to extract video features, and the 3D Adaptive Spatial Pyramid Pooling (3DASPP) module is used to enhance video features. Human body detection is performed on the key frame in the video through the target detection algorithm, and the video features corresponding to the target are extracted. Finally, the human body action category in the video sequence is identified, and whether there is an abnormality is judged. The mean Average Precision (mAP) is used as the evaluation metric. Experimental results show that the proposed algorithm can effectively recognize abnormal human actions in videos, providing strong technical support for intelligent surveillance, intelligent security, and other related fields.