Basketball Fixed-point Shot Hit Prediction Based on Human Pose Estimation Algorithm

Abstract

As computer vision and artificial intelligence develop, the research on basketball fixed-point shot hit prediction based on human pose estimation algorithm becomes a topic of great concern. In order to construct a basketball fixed-point shot hit prediction model, a new object detection algorithm was designed, and the You Only Look Once version 5 algorithm was optimized on the basis of GIoU loss function and Convolutional Block Attention Module. Then, a new human pose estimation algorithm was designed based on the OpenPose algorithm. Results showed that the average accuracy of the improved YOLOv5 algorithm reached 95.34% when the number of iterations was 50. In the comparison between improved OpenPose and other algorithms, improved OpenPose performed better, with a recall rate of 96.23%, an accuracy of 87.16%, an accuracy of 89.75%, and an F1 value of 88.19%. In the comparison with other models, the area of receiver operating characteristic curve was the largest, reaching 0.974, and the F1 value, accuracy and recall rate of the research model were the highest, reaching 95.54%, 96.39% and 98.25%, respectively. Results show that it effectively predicts the shooting rate of basketball fixed-point shooting, which provides a useful reference for tactical analysis and player performance evaluation in basketball games.