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Title: A study on the application of 3DHOG-assisted technology in physical education movement recognition

Authors: Yu He; Na Chen

Addresses: Department of Physical Education and Research, Fuzhou University, Fuzhou, 350108, China ' Ministry of Sports, Xiamen Institute of Technology, Xiamen, 361021, China

Abstract: An image feature extraction technique based on histogram of oriented gradients (HOG) technology is proposed as a method for human body detection, while 3D convolutional neural networks (3D CNN) technology is combined as a key technology for action recognition. And the two are combined to generate 3DHOG assistive technology which is applied to the physical education video parsing. The results show that the false recognition rate of 3D CNN model in the training set is stable around 0.03, corresponding to a loss of 0.05. The average accuracy of each action of 3D HOG model is 96.25%, while the recall rate of the model is 81.2%. Its mean absolute error (MAE) value is 1.18% and root mean squared error (RMSE) value is 0.91%. The 3D HOG model has superior performance and has good application value for action detection and recognition of physical education videos.

Keywords: action recognition; HOG; human detection; physical education; 3D CNN.

DOI: 10.1504/IJCSYSE.2025.145760

International Journal of Computational Systems Engineering, 2025 Vol.9 No.8, pp.1 - 11

Received: 10 Apr 2023
Accepted: 11 Sep 2023
Published online: 23 Apr 2025 *