The Selecting Optimal Ball-Receiving Body Parts Using Pose Sequence Analysis and Sports Biomechanics

Abstract

The purpose is to explore a personalized and targeted training mode in football player training. Firstly, this work introduces the principle and advantages of machine vision sensing. Secondly, from the biomechanical point of view, the influence of the acceleration of several joints and the joint angle on the ball receiving effect is analyzed. Furthermore, the football player’s in-game receiving image is collected using machine vision technology, and the pose image data are preprocessed to construct a data set. Then, a new model is constructed and trained using Haar-like feature (HLF) and (Adaptive Boosting (Adaboost). Finally, the recognition model of the football receiving pose is tested, and the recognition effect is compared with the mainstream recognition model. The results show that the recognition parameter of the traditional method based on the Halcon recognition pose system is 5.12 at 20 times and then begins to decline. In contrast, the identification parameters based on the Industrial Robot Vision System Development (IRVSD) platform are much higher than those based on Halcon. It slightly decreases when the training times are 60 and then gradually increases. However, the recognition parameters based on the proposed machine vision have been far higher than those of the two traditional methods and maintained at about 10. This is because the proposed method extracts the foul image features, establishes the pose sequence potential function, and analyzes it in more detail, thus improving the recognition accuracy. The player’s pose recognition model based on HLF and AdaBoost algorithm can identify and evaluate the ball-receiving pose, thus guiding the receiving improvement. The finding shows that the proposed recognition technology can recognize and evaluate the players’ ball-receiving image, providing a new direction for applying artificial intelligence technology in sports.