Point Tracking Technology of Sports Image Sequence Marks Based on Fuzzy Clustering Algorithm
Fuzzy clustering algorithms have received widespread attention in various fields. Point tracking technology has significant application importance in sports image data analysis. In order to solve the problem of limited tracking performance caused by the fuzzy and rough division of moving image edges...
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| Vydáno v: | Computational intelligence and neuroscience Ročník 2022; s. 1 - 10 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hindawi
28.04.2022
John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | Fuzzy clustering algorithms have received widespread attention in various fields. Point tracking technology has significant application importance in sports image data analysis. In order to solve the problem of limited tracking performance caused by the fuzzy and rough division of moving image edges, this paper proposes a point tracking technology based on a fuzzy clustering algorithm, which is used for the point tracking of moving image sequence signs. This article analyzes the development status of sports image sequence analysis and processing technology and introduces some basic theories about fuzzy clustering algorithms. On the basis of the fuzzy clustering algorithm, the positioning and tracking of the marker points of the moving image sequence are studied. A series of experiments have proved that the fuzzy clustering algorithm can improve the recognition rate of the landmark points of the moving image. For the detection and tracking of moving targets, the fuzzy clustering algorithm can reach the limit faster under the same number of iterations, and the image noise can be reduced to 60% of the original by 5 iterations. This has excellent development value in application. |
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| AbstractList | Fuzzy clustering algorithms have received widespread attention in various fields. Point tracking technology has significant application importance in sports image data analysis. In order to solve the problem of limited tracking performance caused by the fuzzy and rough division of moving image edges, this paper proposes a point tracking technology based on a fuzzy clustering algorithm, which is used for the point tracking of moving image sequence signs. This article analyzes the development status of sports image sequence analysis and processing technology and introduces some basic theories about fuzzy clustering algorithms. On the basis of the fuzzy clustering algorithm, the positioning and tracking of the marker points of the moving image sequence are studied. A series of experiments have proved that the fuzzy clustering algorithm can improve the recognition rate of the landmark points of the moving image. For the detection and tracking of moving targets, the fuzzy clustering algorithm can reach the limit faster under the same number of iterations, and the image noise can be reduced to 60% of the original by 5 iterations. This has excellent development value in application. Fuzzy clustering algorithms have received widespread attention in various fields. Point tracking technology has significant application importance in sports image data analysis. In order to solve the problem of limited tracking performance caused by the fuzzy and rough division of moving image edges, this paper proposes a point tracking technology based on a fuzzy clustering algorithm, which is used for the point tracking of moving image sequence signs. This article analyzes the development status of sports image sequence analysis and processing technology and introduces some basic theories about fuzzy clustering algorithms. On the basis of the fuzzy clustering algorithm, the positioning and tracking of the marker points of the moving image sequence are studied. A series of experiments have proved that the fuzzy clustering algorithm can improve the recognition rate of the landmark points of the moving image. For the detection and tracking of moving targets, the fuzzy clustering algorithm can reach the limit faster under the same number of iterations, and the image noise can be reduced to 60% of the original by 5 iterations. This has excellent development value in application.Fuzzy clustering algorithms have received widespread attention in various fields. Point tracking technology has significant application importance in sports image data analysis. In order to solve the problem of limited tracking performance caused by the fuzzy and rough division of moving image edges, this paper proposes a point tracking technology based on a fuzzy clustering algorithm, which is used for the point tracking of moving image sequence signs. This article analyzes the development status of sports image sequence analysis and processing technology and introduces some basic theories about fuzzy clustering algorithms. On the basis of the fuzzy clustering algorithm, the positioning and tracking of the marker points of the moving image sequence are studied. A series of experiments have proved that the fuzzy clustering algorithm can improve the recognition rate of the landmark points of the moving image. For the detection and tracking of moving targets, the fuzzy clustering algorithm can reach the limit faster under the same number of iterations, and the image noise can be reduced to 60% of the original by 5 iterations. This has excellent development value in application. |
| Audience | Academic |
| Author | Zhang, Dengfeng Li, Yupeng |
| AuthorAffiliation | 2 College of Physical Education and Health, Linyi University, Linyi 276000, Shandong, China 1 Shandong Sport University, Jinan 250102, Shandong, China |
| AuthorAffiliation_xml | – name: 1 Shandong Sport University, Jinan 250102, Shandong, China – name: 2 College of Physical Education and Health, Linyi University, Linyi 276000, Shandong, China |
| Author_xml | – sequence: 1 givenname: Dengfeng surname: Zhang fullname: Zhang, Dengfeng organization: Shandong Sport UniversityJinan 250102ShandongChinasdpei.edu.cn – sequence: 2 givenname: Yupeng orcidid: 0000-0001-6827-1518 surname: Li fullname: Li, Yupeng organization: College of Physical Education and HealthLinyi UniversityLinyi 276000ShandongChinalyu.edu.cn |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35528353$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s11042-019-07787-8 10.1016/j.asoc.2019.03.043 10.1049/el.2019.1281 10.1007/s10489-016-0763-5 10.1016/j.asoc.2015.12.022 10.1007/s11042-015-2860-6 10.1155/2021/2643690 10.1007/s00521-020-05445-x 10.3390/sym13112085 10.1007/s40815-016-0206-9 10.1016/j.knosys.2016.01.001 10.1007/s00256-017-2797-0 10.1615/telecomradeng.v79.i13.80 10.1155/2021/6630130 10.1049/iet-ipr.2016.0282 10.1186/s13640-018-0322-6 10.1007/s13319-016-0089-4 10.3389/fnins.2021.714318 10.1007/s00521-020-05551-w |
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| Copyright | Copyright © 2022 Dengfeng Zhang and Yupeng Li. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Dengfeng Zhang and Yupeng Li. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Dengfeng Zhang and Yupeng Li. 2022 |
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| SubjectTerms | Algorithms Analysis Clustering Data analysis Efficiency Fuzzy sets Image processing Methods Moving targets Noise Object recognition Sequence analysis Sports Sports training Tracking |
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| Title | Point Tracking Technology of Sports Image Sequence Marks Based on Fuzzy Clustering Algorithm |
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