Intelligent Analysis of Sports Video Based on Fuzzy Clustering Algorithm

Because of its huge commercial value and broad application prospects, sports video analysis has attracted extensive attention from researchers at home and abroad, and has become a hot research topic in the field of multimedia. The work of this paper mainly focuses on the extraction of midlevel features of sports videos and the detection and extraction of highlights based on multimodal feature fusion, and on this basis, the exploration of personalized customization is carried out. Drawing on the basic ideas of natural language processing, we the rule-based sports video analysis method is studied, and a grammar-based video analysis system is proposed. First, we decompose the video stream into a basic sequence of shots; then, through event detection, we assign a semantic label to each shot to form a sequence of tokens; finally, through parsing and verification, we generate a directory hierarchy for the video. Compared to existing video analysis systems, our system is not only able to annotate semantic events, but also recognize the hierarchical structure of sports games. Experiments show that with multimodal fusion, our method is better able to handle semantic events in sports videos. On the basis of video semantic analysis and annotation, some exploratory research on personalized customization is carried out, and a set of sports video personalized customization system based on mobile platform is developed, which provides convenient and fast sports video personalization for mobile device users. Customized service.