Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network

Considering the limitation of tracking range on sports video target tracking in basketball games, there are some problems, such as poor tracking effect, low accuracy, low anti-interference ability, and being time-consuming. Therefore, this study proposes a target tracking algorithm of basketball vid...

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Autores principales:	You Jun Wang, Guo Huang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/24bf497d05c548d8b598e5c13e922603
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id	oai:doaj.org-article:24bf497d05c548d8b598e5c13e922603
record_format	dspace
spelling	oai:doaj.org-article:24bf497d05c548d8b598e5c13e9226032021-11-08T02:36:12ZTarget Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network1875-919X10.1155/2021/7808456https://doaj.org/article/24bf497d05c548d8b598e5c13e9226032021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7808456https://doaj.org/toc/1875-919XConsidering the limitation of tracking range on sports video target tracking in basketball games, there are some problems, such as poor tracking effect, low accuracy, low anti-interference ability, and being time-consuming. Therefore, this study proposes a target tracking algorithm of basketball video based on improved grey neural network. According to the pixel grey difference of the target image in basketball video, this study applies the adaptive threshold algorithm in order to segment the target image of basketball video and obtain the target area of basketball video. This algorithm can normalize the grey level of the target area, build the generating sequence of the target area, and collect the target data of the basketball video. It obtains the feature output matrix of basketball video target based on the geometric dispersion of the target image and extracts the key feature points of basketball video target by single frame visual difference analysis. In addition, it makes use of the improved grey neural network to track and locate the feature points of basketball video target and reconstruct the basketball video target image with superresolution to realize basketball video target tracking. The experimental results show that the proposed algorithm has good target tracking effect of basketball video, can effectively improve the target tracking accuracy and anti-interference ability, and can shorten the target tracking time.You Jun WangGuo 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 You Jun Wang Guo Huang Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network
description	Considering the limitation of tracking range on sports video target tracking in basketball games, there are some problems, such as poor tracking effect, low accuracy, low anti-interference ability, and being time-consuming. Therefore, this study proposes a target tracking algorithm of basketball video based on improved grey neural network. According to the pixel grey difference of the target image in basketball video, this study applies the adaptive threshold algorithm in order to segment the target image of basketball video and obtain the target area of basketball video. This algorithm can normalize the grey level of the target area, build the generating sequence of the target area, and collect the target data of the basketball video. It obtains the feature output matrix of basketball video target based on the geometric dispersion of the target image and extracts the key feature points of basketball video target by single frame visual difference analysis. In addition, it makes use of the improved grey neural network to track and locate the feature points of basketball video target and reconstruct the basketball video target image with superresolution to realize basketball video target tracking. The experimental results show that the proposed algorithm has good target tracking effect of basketball video, can effectively improve the target tracking accuracy and anti-interference ability, and can shorten the target tracking time.
format	article
author	You Jun Wang Guo Huang
author_facet	You Jun Wang Guo Huang
author_sort	You Jun Wang
title	Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network
title_short	Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network
title_full	Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network
title_fullStr	Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network
title_full_unstemmed	Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network
title_sort	target tracking algorithm of basketball video based on improved grey neural network
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/24bf497d05c548d8b598e5c13e922603
work_keys_str_mv	AT youjunwang targettrackingalgorithmofbasketballvideobasedonimprovedgreyneuralnetwork AT guohuang targettrackingalgorithmofbasketballvideobasedonimprovedgreyneuralnetwork
_version_	1718443074347073536

Target Tracking Algorithm of Basketball Video Based on Improved Grey Neural Network

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