Shandong Sport University, Jinan 250102, Shandong, China.
College of Physical Education and Health, Linyi University, Linyi 276000, Shandong, China.
Comput Intell Neurosci. 2022 Apr 28;2022:3814252. doi: 10.1155/2022/3814252. eCollection 2022.
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.
模糊聚类算法在各个领域受到广泛关注。点跟踪技术在体育图像数据分析中有重要的应用意义。为了解决运动图像边缘模糊和粗糙划分导致的跟踪性能有限的问题,本文提出了一种基于模糊聚类算法的点跟踪技术,用于运动图像序列标志的点跟踪。本文分析了体育图像序列分析和处理技术的发展现状,并介绍了一些模糊聚类算法的基本理论。在模糊聚类算法的基础上,研究了运动图像序列标志点的定位和跟踪。一系列实验证明,模糊聚类算法可以提高运动图像标志点的识别率。对于运动目标的检测和跟踪,在相同的迭代次数下,模糊聚类算法可以更快地达到极限,并且通过 5 次迭代可以将图像噪声降低到原始噪声的 60%。这在应用中有极好的开发价值。