Abstract
With the rapid development of network and multimedia technology, a large number of sports and national fitness information are stored in various fitness guidance systems in the form of video and pictures. In order to better promote public fitness and facilitate learning and viewing, sports video has a variety of needs of editing, segmentation and integration. Aiming at the shortcomings of current sports video image segmentation methods, such as rough segmentation results and high spatial distortion rate, a sports video image segmentation method based on fuzzy clustering algorithm is proposed. This paper introduces the basic theory of fuzzy clustering algorithm, establishes second-order fuzzy attributes with normal distribution and gray value by means of time-domain difference images, assigns the fuzzy attribute S membership function, then performs fuzzy clustering on time-domain difference images, and obtains the segmentation results of moving video images by edge detection. The experimental results show that the method has high spatial accuracy, good noise iteration performance and low spatial distortion rate, and can accurately segment complex moving video images to obtain high-definition images.
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