Combining AI Techniques for Recognizing Aerobics Sport Videos

In order to improve the recognition accuracy of aerobics athletes’ action features, a multifeature fusion-supported aerobics footprint recognition framework was proposed in this work. By extracting and constructing the 3D peripheral structure reconstruction scheme of the concept of aerobics footprin...

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Main Author: Meiling Duan
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Applied Bionics and Biomechanics
Online Access:http://dx.doi.org/10.1155/2022/2293122
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author Meiling Duan
author_facet Meiling Duan
author_sort Meiling Duan
collection DOAJ
description In order to improve the recognition accuracy of aerobics athletes’ action features, a multifeature fusion-supported aerobics footprint recognition framework was proposed in this work. By extracting and constructing the 3D peripheral structure reconstruction scheme of the concept of aerobics footprint, we combine the fuzzy feature decomposition method to decompose the aerobics footprint image with multi-pane and suggest fusion. We further realize the similarity identification of aerobics athlete’s trajectory through the wear polymorphic liquefaction method. Simulation results have shown that our proposed method has better effect on the similarity recognition of bodybuilders’ footprints, a higher accuracy of behavior location, a satisfactory notification time, and an accurate recognition of bodybuilders’ footprints. Experiments have shown that our designed system has a small mean square error of aerobics movements and a confirmation of absolute failure. It also has a high recognition fidelity. Noticeably, the reason for the difficulty of aerobics coordination of 10 athletes and the confusion of actual coordination is small, and the accuracy of the Beer effect is high. Our method can be applied for body building, and it can provide the foundation for Game Bill.
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spelling doaj-art-fe753865cb1b4e9ea82a01c28f2fd9892025-02-03T05:50:06ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/2293122Combining AI Techniques for Recognizing Aerobics Sport VideosMeiling Duan0Zhengzhou Preschool Education CollegeIn order to improve the recognition accuracy of aerobics athletes’ action features, a multifeature fusion-supported aerobics footprint recognition framework was proposed in this work. By extracting and constructing the 3D peripheral structure reconstruction scheme of the concept of aerobics footprint, we combine the fuzzy feature decomposition method to decompose the aerobics footprint image with multi-pane and suggest fusion. We further realize the similarity identification of aerobics athlete’s trajectory through the wear polymorphic liquefaction method. Simulation results have shown that our proposed method has better effect on the similarity recognition of bodybuilders’ footprints, a higher accuracy of behavior location, a satisfactory notification time, and an accurate recognition of bodybuilders’ footprints. Experiments have shown that our designed system has a small mean square error of aerobics movements and a confirmation of absolute failure. It also has a high recognition fidelity. Noticeably, the reason for the difficulty of aerobics coordination of 10 athletes and the confusion of actual coordination is small, and the accuracy of the Beer effect is high. Our method can be applied for body building, and it can provide the foundation for Game Bill.http://dx.doi.org/10.1155/2022/2293122
spellingShingle Meiling Duan
Combining AI Techniques for Recognizing Aerobics Sport Videos
Applied Bionics and Biomechanics
title Combining AI Techniques for Recognizing Aerobics Sport Videos
title_full Combining AI Techniques for Recognizing Aerobics Sport Videos
title_fullStr Combining AI Techniques for Recognizing Aerobics Sport Videos
title_full_unstemmed Combining AI Techniques for Recognizing Aerobics Sport Videos
title_short Combining AI Techniques for Recognizing Aerobics Sport Videos
title_sort combining ai techniques for recognizing aerobics sport videos
url http://dx.doi.org/10.1155/2022/2293122
work_keys_str_mv AT meilingduan combiningaitechniquesforrecognizingaerobicssportvideos