Design of volleyball action recognition method integrating human posture and spatiotemporal graph convolution

With the rise of intelligent algorithms, automated motion recognition in sports like volleyball-known for its complex movements-remains challenging. To enhance accuracy, this study introduces the Human Posture and Spatiotemporal Graph Convolution (HP-SGC) model. It uses skeletal keypoints to create a 2D coordinate system, combines object detection and pose estimation for action recognition, and applies spatiotemporal graph convolution for classification. The tests showed over 95% accuracy for continuous actions, with recognition time as low as 2.2 seconds. The model successfully identified 8 foul actions, 7 basic moves, and 4 skillful actions, averaging >90% accuracy. These results demonstrate HP-SGC's strong performance in volleyball action recognition, offering valuable tools for match analysis and statistics.