Application of Skeleton Image Detection in Basketball Free Throw Posture Research

With the breakthroughs in deep learning technology, human pose estimation has emerged as a critical research area in computer vision. Its wide-ranging applications include posture assessment, posture correction, sports coaching, and injury prevention. This study explores the application of human pose estimation techniques in sports science, utilizing OpenPose and YOLOv5 models to capture basketball players' limbs and the shooting postures of basketball players when shooting. Based on these measured data, the accuracy of their shooting techniques will be analyzed based on standards established based on academic research and the experience of coaching professionals. Finally, the machine learning classification model was used to distinguish the quality of the basketball shooting process, achieving an accuracy rate of 87.69%.