| Title |
Key Posture Recognition and Analysis of Sports Basketball Based on Improved YOLOv3 Algorithm |
| Authors |
(Shunmin Su) ; (Shuangshuang Yan) |
| DOI |
https://doi.org/10.5573/IEIESPC.2026.15.1.55 |
| Keywords |
YOLOv3; Sport of basketball; Gesture recognition; Enhancement techniques |
| Abstract |
In this study, a key gesture recognition method of basketball based on improved YOLOv3 algorithm is proposed. By optimizing the algorithm, by improving the feature extraction network and introducing spatiotemporal information, we construct a data set containing a large number of basketball actions for experimental verification.
The enhanced algorithm demonstrated a 92% success rate in recognizing crucial poses, outperforming the original YOLOv3 algorithm by 10%, and shows higher efficiency in real-time video processing, and the average processing time per frame is shortened by 20%. The improved YOLOv3 algorithm shows high performance in basketball’s key gesture recognition tasks, and provides a new technical means for scientific training and competition analysis of basketball. The algorithm combines the spatial domain of convolutional networks to enhance the sensory field of the model and extracts the data of human feature points from two dimensions to improve the deficiency of the model fitting ability. Meanwhile, a multi-head attention mechanism is used to enhance the model adaptive capability. In the experimental tests, the proposed model improvement term has a certain performance improvement compared with the traditional algorithm, and the error index of the model is reduced by 1.15 mm and the accuracy is improved by 2.3% compared with the optimal algorithm, which proves the effectiveness and superiority of the proposed method. |