Recognition of Basketball Player’s Shooting Action Based on the Convolutional Neural Network

In the field of basketball, the formulation of the existing training plan mainly relies on the coaches’ artificial observation and personal experience, which is inevitably subjective. The application of body domain network technology in athletes’ training and recognition of athletes’ postures can he...

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Main Authors: Rui Liu, Ziqi Liu, Shuyong Liu
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Scientific Programming
Online Access:http://dx.doi.org/10.1155/2021/3045418
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spelling doaj-e1e5ecd612434171ba0fbadfadd9574b2021-07-05T00:02:37ZengHindawi LimitedScientific Programming1875-919X2021-01-01202110.1155/2021/3045418Recognition of Basketball Player’s Shooting Action Based on the Convolutional Neural NetworkRui Liu0Ziqi Liu1Shuyong Liu2Lingnan Normal UniversityInstitute Genetics and Developmental BiologyP. E. Scientific CollegeIn the field of basketball, the formulation of the existing training plan mainly relies on the coaches’ artificial observation and personal experience, which is inevitably subjective. The application of body domain network technology in athletes’ training and recognition of athletes’ postures can help coaches to assist decision-making and greatly improve athletes’ competitive ability. The human movements reflected in basketball are more complex which need deep understanding. The accuracy of basketball players’ shooting movements recognition plays a positive and important role in basketball games and training practice. Based on the prior knowledge of the convolutional neural network study, environment light conditions change the dynamic characteristics of basketball image analysis, capture images of the basketball goal algorithm of minimum circumscribed rectangle of the object, and based on the convolutional neural network, introduce two types of prior knowledge, one kind is based on the feature matching method that defined a priori knowledge, while another kind is based on training the convolution neural network model. The test results of the network model are taken as the prior knowledge, and then, a convolutional neural network dynamic target recognition model is constructed based on the prior knowledge. The construction process of the model is organized as the basketball target image is collected under any illumination conditions, the convolutional neural network model is trained with the convolutional neural network as the input data, and the standard illumination conditions are determined according to the test results of the network model. Then, put it into the trained network model to test and get the recognition results of basketball players’ shooting movements. The research is validated with performing experiments and the results revealed the success of the study.http://dx.doi.org/10.1155/2021/3045418
collection DOAJ
language English
format Article
sources DOAJ
author Rui Liu
Ziqi Liu
Shuyong Liu
spellingShingle Rui Liu
Ziqi Liu
Shuyong Liu
Recognition of Basketball Player’s Shooting Action Based on the Convolutional Neural Network
Scientific Programming
author_facet Rui Liu
Ziqi Liu
Shuyong Liu
author_sort Rui Liu
title Recognition of Basketball Player’s Shooting Action Based on the Convolutional Neural Network
title_short Recognition of Basketball Player’s Shooting Action Based on the Convolutional Neural Network
title_full Recognition of Basketball Player’s Shooting Action Based on the Convolutional Neural Network
title_fullStr Recognition of Basketball Player’s Shooting Action Based on the Convolutional Neural Network
title_full_unstemmed Recognition of Basketball Player’s Shooting Action Based on the Convolutional Neural Network
title_sort recognition of basketball player’s shooting action based on the convolutional neural network
publisher Hindawi Limited
series Scientific Programming
issn 1875-919X
publishDate 2021-01-01
description In the field of basketball, the formulation of the existing training plan mainly relies on the coaches’ artificial observation and personal experience, which is inevitably subjective. The application of body domain network technology in athletes’ training and recognition of athletes’ postures can help coaches to assist decision-making and greatly improve athletes’ competitive ability. The human movements reflected in basketball are more complex which need deep understanding. The accuracy of basketball players’ shooting movements recognition plays a positive and important role in basketball games and training practice. Based on the prior knowledge of the convolutional neural network study, environment light conditions change the dynamic characteristics of basketball image analysis, capture images of the basketball goal algorithm of minimum circumscribed rectangle of the object, and based on the convolutional neural network, introduce two types of prior knowledge, one kind is based on the feature matching method that defined a priori knowledge, while another kind is based on training the convolution neural network model. The test results of the network model are taken as the prior knowledge, and then, a convolutional neural network dynamic target recognition model is constructed based on the prior knowledge. The construction process of the model is organized as the basketball target image is collected under any illumination conditions, the convolutional neural network model is trained with the convolutional neural network as the input data, and the standard illumination conditions are determined according to the test results of the network model. Then, put it into the trained network model to test and get the recognition results of basketball players’ shooting movements. The research is validated with performing experiments and the results revealed the success of the study.
url http://dx.doi.org/10.1155/2021/3045418
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AT ziqiliu recognitionofbasketballplayersshootingactionbasedontheconvolutionalneuralnetwork
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