A Tennis Training Action Analysis Model Based on Graph Convolutional Neural Network

With the development of artificial intelligence technology, an increasing number of human action recognition (HAR) methods are being applied to tennis training action analysis. The HAR methods based on skeletal points have been extensively researched and applied due to their superior action expression capabilities. In order to enhance the HAR ability of tennis players and effectively capture the detailed features in training actions, this paper proposes a tennis training action analysis model based on graph convolutional neural networks. Firstly, this paper establishes the limb vectors of humans in three-dimensional spatial coordinates and extracts the features of tennis error techniques based on the distances between the skeletal joints of five parts of the human body. Secondly, the data’s time frames are segmented to extract attention and improve the model’s ability to capture detailed features. Additionally, the attention mechanism is introduced to embed the position information into the attention map, enhancing the model’s generalization ability. Experiments conducted on several action datasets demonstrate that the proposed model in this paper achieves higher HAR accuracy and better recognition results compared to most current methods.