JACIII Vol.28 No.3 pp. 552-561
doi: 10.20965/jaciii.2024.p0552

Research Paper:

Basketball Sports Posture Recognition Technology Based on Improved Graph Convolutional Neural Network

Jinmao Tong* and Fei Wang**,†

*College of General Education, Fujian Chuanzheng Communications College
No.80 Shoushan Road, Cangshan District, Fuzhou, Fujian 350007, China

**Ministry of Sports, Xiamen Institute of Technology
No.1251 Sunban South Road, Jimei District, Xiamen, Fujian 361021, China

Corresponding author

October 9, 2023
January 10, 2024
May 20, 2024
GCN, ST-GCN, basketball technical action, gesture recognition, spatial graph convolution

Basketball has rapidly developed in recent years. Analysis of various moves in basketball can provide technical references for professional players and assist referees in judging games. Traditional technology can no longer provide modern basketball players with theoretical support. Therefore, using intelligent methods to recognize human body postures in basketball was a relatively innovative approach. To be able to recognize the basketball sports posture of players more accurately, the experiment proposes a basketball stance recognition model based on enhanced graph convolutional networks (GCN), that is, the basketball stance recognition model based on enhanced GCN and spatial temporal graph convolutional network (ST-GCN) model. This model combines the respective advantages of the GCN and temporal convolutional network and can handle graph-structured data with time-series relationships. The ST-GCN can be further deduced by realizing the convolution operation of the graph structure and establishing a spatiotemporal graph convolution model for the posture sequence of a person’s body. A dataset of technical basketball actions is constructed to verify the effectiveness of the ST-GCN model. The final experimental findings indicated that the final recognition accuracy of the ST-GCN model for basketball postures was approximately 95.58%, whereas the final recognition accuracy of the long short term memory + multiview re-observation skeleton action recognition (LSTM+MV+AC) model was about 93.65%.

A basketball posture recognition model

A basketball posture recognition model

Cite this article as:
J. Tong and F. Wang, “Basketball Sports Posture Recognition Technology Based on Improved Graph Convolutional Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 552-561, 2024.
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Last updated on Jun. 03, 2024