Video abnormal action detection based on enhanced video Swin transformer

Anheng Xie; Longye Wang

doi:10.1117/12.2684788

19 October 2023 Video abnormal action detection based on enhanced video Swin transformer

Anheng Xie, Longye Wang

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127092H (2023) https://doi.org/10.1117/12.2684788
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

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.

Citation Download Citation

Anheng Xie and Longye Wang "Video abnormal action detection based on enhanced video Swin transformer", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127092H (19 October 2023); https://doi.org/10.1117/12.2684788

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