Computer vision systems and deep learning for the recognition of athlete's movement: A review article

Abstract

The process of detecting people in videos and then tracking their movement is one of the very important topics. The process of tracking people and studying their behaviour could result in a large set of information that can help researchers in studying reactions. The techniques of detection and tracking the movement of people are used in the sports field, where the athlete's movement is studied and analyzed within the game. Based on the information obtained from the process of tracking the athlete's movement, it is possible to improve the playing performance as well as avoid injuries and choose the best playing strategy. In some games, the accuracy of athlete's performance is a measure of the points given to the athlete's like gymnastics. This study reviews a set of articles that relied on computer vision as well as deep learning in the process of distinguishing and analysing the athlete's movement. The articles are confined to the years from 2015 to 2022, dealing with different indoor and outdoor sports. Certainly, the study of indoor games is better because the influence of weather conditions is less than that of outdoor ones. Reviewing the articles demonstrates that relying on computer vision systems is more effective than relying on human rulers, as humans are more prone to error. As for relying on the deep learning techniques for detecting the object, the results are very positive, due to the correct detection of the object. The results of the analysis of this object will be more accurate.