Neural Network-Oriented Big Data Model for Yoga Movement Recognition

The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Hui Wang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/5f313dddfbe543679755bda66eab90c1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5f313dddfbe543679755bda66eab90c1
record_format	dspace
spelling	oai:doaj.org-article:5f313dddfbe543679755bda66eab90c12021-11-08T02:35:48ZNeural Network-Oriented Big Data Model for Yoga Movement Recognition1687-527310.1155/2021/4334024https://doaj.org/article/5f313dddfbe543679755bda66eab90c12021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4334024https://doaj.org/toc/1687-5273The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a need has motivated researchers to combine artificial intelligence with the field of sports to conduct research. In this paper, we propose a Mask Region-Convolutional Neural Network (MR-CNN)- based method for yoga movement recognition based on the image task of yoga movement recognition. The improved MR-CNN model is based on the framework and structure of the region-convolutional network, which proposes a certain number of candidate regions for the image by feature extraction and classifies them, then outputs these regions as detected bounding boxes, and does mask prediction for the candidate regions using segmentation branches. The improved MR-CNN model uses an improved deep residual network as the backbone network for feature extraction, bilinear interpolation of the extracted candidate regions using Region of Interest (RoI) Align, followed by target classification and detection, and segmentation of the image using the segmentation branch. The model improves the convolution part in the segmentation branch by replacing the original standard convolution with a depth-separable convolution to improve the network efficiency. Experimentally constructed polygon-labeled datasets are simulated using the algorithm. The deepening of the network and the use of depth-separable network improve the accuracy of detection while maintaining the reliability of the network and validate the effectiveness of the improved MR-CNN.Hui WangHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Hui Wang Neural Network-Oriented Big Data Model for Yoga Movement Recognition
description	The use of computer vision for target detection and recognition has been an interesting and challenging area of research for the past three decades. Professional athletes and sports enthusiasts in general can be trained with appropriate systems for corrective training and assistive training. Such a need has motivated researchers to combine artificial intelligence with the field of sports to conduct research. In this paper, we propose a Mask Region-Convolutional Neural Network (MR-CNN)- based method for yoga movement recognition based on the image task of yoga movement recognition. The improved MR-CNN model is based on the framework and structure of the region-convolutional network, which proposes a certain number of candidate regions for the image by feature extraction and classifies them, then outputs these regions as detected bounding boxes, and does mask prediction for the candidate regions using segmentation branches. The improved MR-CNN model uses an improved deep residual network as the backbone network for feature extraction, bilinear interpolation of the extracted candidate regions using Region of Interest (RoI) Align, followed by target classification and detection, and segmentation of the image using the segmentation branch. The model improves the convolution part in the segmentation branch by replacing the original standard convolution with a depth-separable convolution to improve the network efficiency. Experimentally constructed polygon-labeled datasets are simulated using the algorithm. The deepening of the network and the use of depth-separable network improve the accuracy of detection while maintaining the reliability of the network and validate the effectiveness of the improved MR-CNN.
format	article
author	Hui Wang
author_facet	Hui Wang
author_sort	Hui Wang
title	Neural Network-Oriented Big Data Model for Yoga Movement Recognition
title_short	Neural Network-Oriented Big Data Model for Yoga Movement Recognition
title_full	Neural Network-Oriented Big Data Model for Yoga Movement Recognition
title_fullStr	Neural Network-Oriented Big Data Model for Yoga Movement Recognition
title_full_unstemmed	Neural Network-Oriented Big Data Model for Yoga Movement Recognition
title_sort	neural network-oriented big data model for yoga movement recognition
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/5f313dddfbe543679755bda66eab90c1
work_keys_str_mv	AT huiwang neuralnetworkorientedbigdatamodelforyogamovementrecognition
_version_	1718443188081917952

Neural Network-Oriented Big Data Model for Yoga Movement Recognition

Ejemplares similares