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Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection

The rapid development of Internet of things mobile application technology and artificial intelligence technology has given birth to a lot of services that can meet the needs of modern life, such as augmented reality technology, face recognition services, and language recognition and translation, whi...

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Autor principal: Li, Hongtao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550408/
https://www.ncbi.nlm.nih.gov/pubmed/36225544
http://dx.doi.org/10.1155/2022/7423411
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author Li, Hongtao
author_facet Li, Hongtao
author_sort Li, Hongtao
collection PubMed
description The rapid development of Internet of things mobile application technology and artificial intelligence technology has given birth to a lot of services that can meet the needs of modern life, such as augmented reality technology, face recognition services, and language recognition and translation, which are often applied to various fields, and some other aspects of information communication and processing services. It has been used on various mobile phone, computer, or tablet user clients. Terminal equipment is subject to the ultralow latency and low energy consumption requirements of the above-mentioned applications. Therefore, the gap between resource-demanding application services and resource-limited mobile devices will bring great problems to the current and future development of IoT mobile applications. Based on the local image features of depth images, this paper designs an image detection method for athletes' motion posture. First, according to the characteristics of the local image, the depth image of the athlete obtained through Kinect is converted into bone point data. Next, a 3-stage exploration algorithm is used to perform block matching calculations on the athlete's bone point image to predict the athlete's movement posture. At the same time, using the characteristics of the Euclidean distance of the bone point image, the movement behavior is recognized. According to the experimental results, for some external environmental factors, such as sun illumination and other factors, the image detection method designed in this paper can effectively avoid their interference and influence and show the movement posture of athletes, showing excellent accuracy and robustness in predicting the movement posture of athletes and action recognition. This method can simplify a series of calibration tasks in the initial stage of 3D video surveillance and infer the posture of the observation target and recognize it in real time. The one that has good application values has specific reference values for the same job.
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spelling pubmed-95504082022-10-11 Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection Li, Hongtao Comput Intell Neurosci Research Article The rapid development of Internet of things mobile application technology and artificial intelligence technology has given birth to a lot of services that can meet the needs of modern life, such as augmented reality technology, face recognition services, and language recognition and translation, which are often applied to various fields, and some other aspects of information communication and processing services. It has been used on various mobile phone, computer, or tablet user clients. Terminal equipment is subject to the ultralow latency and low energy consumption requirements of the above-mentioned applications. Therefore, the gap between resource-demanding application services and resource-limited mobile devices will bring great problems to the current and future development of IoT mobile applications. Based on the local image features of depth images, this paper designs an image detection method for athletes' motion posture. First, according to the characteristics of the local image, the depth image of the athlete obtained through Kinect is converted into bone point data. Next, a 3-stage exploration algorithm is used to perform block matching calculations on the athlete's bone point image to predict the athlete's movement posture. At the same time, using the characteristics of the Euclidean distance of the bone point image, the movement behavior is recognized. According to the experimental results, for some external environmental factors, such as sun illumination and other factors, the image detection method designed in this paper can effectively avoid their interference and influence and show the movement posture of athletes, showing excellent accuracy and robustness in predicting the movement posture of athletes and action recognition. This method can simplify a series of calibration tasks in the initial stage of 3D video surveillance and infer the posture of the observation target and recognize it in real time. The one that has good application values has specific reference values for the same job. Hindawi 2022-10-03 /pmc/articles/PMC9550408/ /pubmed/36225544 http://dx.doi.org/10.1155/2022/7423411 Text en Copyright © 2022 Hongtao Li. 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, Hongtao
Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection
title Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection
title_full Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection
title_fullStr Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection
title_full_unstemmed Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection
title_short Cloud Computing Image Processing Application in Athlete Training High-Resolution Image Detection
title_sort cloud computing image processing application in athlete training high-resolution image detection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550408/
https://www.ncbi.nlm.nih.gov/pubmed/36225544
http://dx.doi.org/10.1155/2022/7423411
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