Research on Sports Video Image Analysis Based on the Fuzzy Clustering Algorithm

Abstract

Aimed at the shortcomings of the current sports video image segmentation methods, such as rough image segmentation results and high spatial distortion rate, a sports video image segmentation method based on a fuzzy clustering algorithm is proposed. The second-order fuzzy attribute with normal distribution and gravity value is established by using the time-domain difference image, and the membership function of the fuzzy attribute is given; then, the time-domain difference image is fuzzy clustered, and the motion video image segmentation result is obtained by edge detection. Experimental results show that this method has high spatial accuracy, good noise iteration performance, and low spatial distortion rate and can accurately segment complex moving video images and obtain high-definition images. The application of this video image analysis method will help master the rules of sports technology and the characteristics of healthy people’s sports skills through video image analysis and help improve physical education, national fitness level, and competitive sports level.