Sports Intelligent Assistance System Based on Deep Learning

Traditional sports aid systems analyze sports data via sensors and other types of equipment and can support athletes with retrospective analysis, but they require several sensors and have limited data. This paper examines a sports aid system that uses deep learning to recognize, review, and analyze...

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Autor principal:	Boyin Wu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/947046e5b1d943738e88046cf7b92f69
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spelling	oai:doaj.org-article:947046e5b1d943738e88046cf7b92f692021-11-29T00:56:57ZSports Intelligent Assistance System Based on Deep Learning1875-919X10.1155/2021/3481469https://doaj.org/article/947046e5b1d943738e88046cf7b92f692021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3481469https://doaj.org/toc/1875-919XTraditional sports aid systems analyze sports data via sensors and other types of equipment and can support athletes with retrospective analysis, but they require several sensors and have limited data. This paper examines a sports aid system that uses deep learning to recognize, review, and analyze behaviors through video acquisition and intelligent video sequence processing. This paper’s primary research is as follows: (1) With an eye on the motion assistance system’s application scenarios, the network topology and implementation details of the two-stage Faster R-CNN and the single-stage YOLOv3 target detection algorithms are investigated. Additionally, training procedures are used to enhance the algorithm’s detection performance and training speed. (2) To address the issue of target detection techniques’ low detection performance in complicated backgrounds, an improved scheme from Faster R-CNN is proposed. To begin, a new approach replaces the VGG-16 network in the previous algorithm with a ResNet-101 network. Second, an expansion plan for the dataset is provided. (3) To address the short duration of action video and the high correlation of image sequence data, we present an action recognition method based on LSTM. To begin, we will present a motion decomposition scheme and evaluation index based on the key transaction frame in order to simplify the motion analysis procedure. Second, the spatial features of the frame images are extracted using a convolutional neural network. Besides, the spatial and temporal aspects of the image sequence are fused using a two-layer bidirectional LSTM network. The algorithm suggested in this research has been validated using a golf experiment, and the results are favorable.Boyin WuHindawi 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 Boyin Wu Sports Intelligent Assistance System Based on Deep Learning
description	Traditional sports aid systems analyze sports data via sensors and other types of equipment and can support athletes with retrospective analysis, but they require several sensors and have limited data. This paper examines a sports aid system that uses deep learning to recognize, review, and analyze behaviors through video acquisition and intelligent video sequence processing. This paper’s primary research is as follows: (1) With an eye on the motion assistance system’s application scenarios, the network topology and implementation details of the two-stage Faster R-CNN and the single-stage YOLOv3 target detection algorithms are investigated. Additionally, training procedures are used to enhance the algorithm’s detection performance and training speed. (2) To address the issue of target detection techniques’ low detection performance in complicated backgrounds, an improved scheme from Faster R-CNN is proposed. To begin, a new approach replaces the VGG-16 network in the previous algorithm with a ResNet-101 network. Second, an expansion plan for the dataset is provided. (3) To address the short duration of action video and the high correlation of image sequence data, we present an action recognition method based on LSTM. To begin, we will present a motion decomposition scheme and evaluation index based on the key transaction frame in order to simplify the motion analysis procedure. Second, the spatial features of the frame images are extracted using a convolutional neural network. Besides, the spatial and temporal aspects of the image sequence are fused using a two-layer bidirectional LSTM network. The algorithm suggested in this research has been validated using a golf experiment, and the results are favorable.
format	article
author	Boyin Wu
author_facet	Boyin Wu
author_sort	Boyin Wu
title	Sports Intelligent Assistance System Based on Deep Learning
title_short	Sports Intelligent Assistance System Based on Deep Learning
title_full	Sports Intelligent Assistance System Based on Deep Learning
title_fullStr	Sports Intelligent Assistance System Based on Deep Learning
title_full_unstemmed	Sports Intelligent Assistance System Based on Deep Learning
title_sort	sports intelligent assistance system based on deep learning
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/947046e5b1d943738e88046cf7b92f69
work_keys_str_mv	AT boyinwu sportsintelligentassistancesystembasedondeeplearning
_version_	1718407660261343232

Sports Intelligent Assistance System Based on Deep Learning

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