Computer vision systems and deep learning for the recognition of athlete's movement: A review article
DOI:
https://doi.org/10.25130/tjps.v28i6.1600Keywords:
Computer vision, deep learning, athlete's movement analysis, object recognition.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.
Downloads
Published
How to Cite
License
Copyright (c) 2023 THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE http://creativecommons.org/licenses/by/4.0/
This work is licensed under a Creative Commons Attribution 4.0 International License.
Tikrit Journal of Pure Science is licensed under the Creative Commons Attribution 4.0 International License, which allows users to copy, create extracts, abstracts, and new works from the article, alter and revise the article, and make commercial use of the article (including reuse and/or resale of the article by commercial entities), provided the user gives appropriate credit (with a link to the formal publication through the relevant DOI), provides a link to the license, indicates if changes were made, and the licensor is not represented as endorsing the use made of the work. The authors hold the copyright for their published work on the Tikrit J. Pure Sci. website, while Tikrit J. Pure Sci. is responsible for appreciate citation of their work, which is released under CC-BY-4.0, enabling the unrestricted use, distribution, and reproduction of an article in any medium, provided that the original work is properly cited.
