Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video

Football is one of the favorite sports of people nowadays. Shooting is the ultimate goal of all offensive tactics in football matches. This is the most basic way to score a goal and the only way to score a goal. The choice and use of shooting technical indicators can have a great impact on the final...

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Autores principales:	Guangliang Huang, Zhuangxu Lan, Guo Huang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/2249b1971b2145f8a4bf0cc61cfacd65
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spelling	oai:doaj.org-article:2249b1971b2145f8a4bf0cc61cfacd652021-11-08T02:35:17ZFootball Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video1875-919X10.1155/2021/1552096https://doaj.org/article/2249b1971b2145f8a4bf0cc61cfacd652021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1552096https://doaj.org/toc/1875-919XFootball is one of the favorite sports of people nowadays. Shooting is the ultimate goal of all offensive tactics in football matches. This is the most basic way to score a goal and the only way to score a goal. The choice and use of shooting technical indicators can have a great impact on the final result of the game. Therefore, how to improve the shooting technique of football players and how to adjust the shooting posture of football players are important issues faced by coaches and athletes. In recent years, deep learning has been widely used in various fields such as image classification and recognition and language processing. How to apply deep learning optimization to shooting gesture recognition is a very promising research direction. This article aims to study the football player’s shooting posture specification based on deep learning in sports event videos. Based on the analysis of target motion detection algorithm, target motion tracking algorithm, target motion recognition algorithm, and football shooting posture classification, KTH and Weizmann data sets are used. As the experimental verification data set of this article, the shooting posture of football players in the sports event video is recognized, and the accuracy of the action recognition is finally calculated to standardize the football shooting posture. The experimental results show that the Weizmann data set has a higher accuracy rate than the KTH data set and is more suitable for shooting attitude specifications.Guangliang HuangZhuangxu LanGuo HuangHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Guangliang Huang Zhuangxu Lan Guo Huang Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video
description	Football is one of the favorite sports of people nowadays. Shooting is the ultimate goal of all offensive tactics in football matches. This is the most basic way to score a goal and the only way to score a goal. The choice and use of shooting technical indicators can have a great impact on the final result of the game. Therefore, how to improve the shooting technique of football players and how to adjust the shooting posture of football players are important issues faced by coaches and athletes. In recent years, deep learning has been widely used in various fields such as image classification and recognition and language processing. How to apply deep learning optimization to shooting gesture recognition is a very promising research direction. This article aims to study the football player’s shooting posture specification based on deep learning in sports event videos. Based on the analysis of target motion detection algorithm, target motion tracking algorithm, target motion recognition algorithm, and football shooting posture classification, KTH and Weizmann data sets are used. As the experimental verification data set of this article, the shooting posture of football players in the sports event video is recognized, and the accuracy of the action recognition is finally calculated to standardize the football shooting posture. The experimental results show that the Weizmann data set has a higher accuracy rate than the KTH data set and is more suitable for shooting attitude specifications.
format	article
author	Guangliang Huang Zhuangxu Lan Guo Huang
author_facet	Guangliang Huang Zhuangxu Lan Guo Huang
author_sort	Guangliang Huang
title	Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video
title_short	Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video
title_full	Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video
title_fullStr	Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video
title_full_unstemmed	Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video
title_sort	football players’ shooting posture norm based on deep learning in sports event video
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/2249b1971b2145f8a4bf0cc61cfacd65
work_keys_str_mv	AT guanglianghuang footballplayersshootingposturenormbasedondeeplearninginsportseventvideo AT zhuangxulan footballplayersshootingposturenormbasedondeeplearninginsportseventvideo AT guohuang footballplayersshootingposturenormbasedondeeplearninginsportseventvideo
_version_	1718443263238602752

Football Players’ Shooting Posture Norm Based on Deep Learning in Sports Event Video

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