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Point Tracking Technology of Sports Image Sequence Marks Based on Fuzzy Clustering Algorithm.
1Shandong Sport University, Jinan 250102, Shandong, China.
Computational Intelligence and Neuroscience
|May 9, 2022
Summary
This study introduces a fuzzy clustering algorithm for improved point tracking in sports image analysis. The fuzzy clustering method enhances landmark recognition and speeds up moving target detection while reducing image noise.
Area of Science:
- Computer Vision
- Pattern Recognition
- Data Analysis
Background:
- Fuzzy clustering algorithms are widely used across various domains.
- Point tracking technology is crucial for sports image data analysis.
- Existing methods struggle with fuzzy edges in moving images, limiting tracking performance.
Purpose of the Study:
- To propose a novel point tracking technology using a fuzzy clustering algorithm.
- To address limitations in tracking performance caused by imprecise edge detection in moving image sequences.
- To enhance the recognition rate of landmark points in sports imagery.
Main Methods:
- Analysis of sports image sequence analysis and processing technologies.
- Introduction to the fundamental theories of fuzzy clustering algorithms.
- Application of fuzzy clustering for positioning and tracking marker points in moving image sequences.
Main Results:
- Fuzzy clustering significantly improves the recognition rate of moving image landmark points.
- The algorithm converges faster for moving target detection and tracking compared to other methods.
- Image noise is reduced by up to 60% within 5 iterations of the fuzzy clustering algorithm.
Conclusions:
- The proposed fuzzy clustering-based point tracking technology offers excellent development potential for sports image analysis.
- This method effectively overcomes challenges associated with fuzzy image edges.
- The algorithm demonstrates efficiency in both target recognition and noise reduction.
