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. 2022 Apr 28:2022:3814252.
doi: 10.1155/2022/3814252. eCollection 2022.

Point Tracking Technology of Sports Image Sequence Marks Based on Fuzzy Clustering Algorithm

Dengfeng Zhang  1 , Yupeng Li  2
Affiliations

Point Tracking Technology of Sports Image Sequence Marks Based on Fuzzy Clustering Algorithm

Dengfeng Zhang et al. Comput Intell Neurosci. .

Retraction in

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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Conflict of interest statement

The authors declare that there are no conflicts of interest with any financial organizations regarding the material reported in this manuscript.

Figures

Figure 1
Figure 1
Basic composition diagram of moving target tracking.
Figure 2
Figure 2
Schematic diagram of moving target detection and tracking.
Figure 3
Figure 3
Image noise average curve.
Figure 4
Figure 4
The relationship between the number of iterations of segment 1 and segment 2 and the average value of noise.
Figure 5
Figure 5
Target tracking result of a table tennis match.
Figure 6
Figure 6
Multitarget tracking results under the two algorithms.
See this image and copyright information in PMC

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