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Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network

Artificial intelligence and deep learning have attracted much attention from researchers in industry and academia. The volleyball movement standardization and recognition model involve the application of artificial intelligence and deep learning. In order to solve the problem that human action in vo...

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Detalles Bibliográficos
Autores principales: Li, Baiyu, Tian, Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891813/
https://www.ncbi.nlm.nih.gov/pubmed/36744120
http://dx.doi.org/10.1155/2023/6116144
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author Li, Baiyu
Tian, Ming
author_facet Li, Baiyu
Tian, Ming
author_sort Li, Baiyu
collection PubMed
description Artificial intelligence and deep learning have attracted much attention from researchers in industry and academia. The volleyball movement standardization and recognition model involve the application of artificial intelligence and deep learning. In order to solve the problem that human action in volleyball video is continuous and effective spatial and temporal features need to be extracted from the video stream, the Inception module is decoupled and heterogeneous, replacing the original 5 × 5 convolutional structures with two 3 × 3 convolutional structures, as well as replacing the 3 × 3 convolutional structures with 1 × 3 and a 3 × 1 convolutional structure with internal parameter optimization to ensure the accuracy of recognition. The model uses the input motion video RGB map as the spatial input and the optical flow map as the temporal input, and the two are weighted 1 : 1 for feature fusion. Experiments are conducted on the volleyball action video and homemade dataset in UCF101, and the experimental data show that the accuracy of the DNet volleyball action standardization recognition model proposed in this paper is 94.12%, which proves that the method improves the recognition ability of the model while speeding up the training speed. The research presented in this paper provides important theoretical guidance for artificial intelligence and deep learning.
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spelling pubmed-98918132023-02-02 Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network Li, Baiyu Tian, Ming Comput Intell Neurosci Research Article Artificial intelligence and deep learning have attracted much attention from researchers in industry and academia. The volleyball movement standardization and recognition model involve the application of artificial intelligence and deep learning. In order to solve the problem that human action in volleyball video is continuous and effective spatial and temporal features need to be extracted from the video stream, the Inception module is decoupled and heterogeneous, replacing the original 5 × 5 convolutional structures with two 3 × 3 convolutional structures, as well as replacing the 3 × 3 convolutional structures with 1 × 3 and a 3 × 1 convolutional structure with internal parameter optimization to ensure the accuracy of recognition. The model uses the input motion video RGB map as the spatial input and the optical flow map as the temporal input, and the two are weighted 1 : 1 for feature fusion. Experiments are conducted on the volleyball action video and homemade dataset in UCF101, and the experimental data show that the accuracy of the DNet volleyball action standardization recognition model proposed in this paper is 94.12%, which proves that the method improves the recognition ability of the model while speeding up the training speed. The research presented in this paper provides important theoretical guidance for artificial intelligence and deep learning. Hindawi 2023-01-25 /pmc/articles/PMC9891813/ /pubmed/36744120 http://dx.doi.org/10.1155/2023/6116144 Text en Copyright © 2023 Baiyu Li and Ming Tian. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Li, Baiyu
Tian, Ming
Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network
title Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network
title_full Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network
title_fullStr Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network
title_full_unstemmed Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network
title_short Volleyball Movement Standardization Recognition Model Based on Convolutional Neural Network
title_sort volleyball movement standardization recognition model based on convolutional neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9891813/
https://www.ncbi.nlm.nih.gov/pubmed/36744120
http://dx.doi.org/10.1155/2023/6116144
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