Classification of Tennis Video Types Based on Machine Learning Technology

Abstract

With the rapid development of online video data, how to find the required information has become an urgent problem to be solved. This article focuses on sports videos and studies video classification and content-based retrieval techniques. Its purpose is to establish a mark and index of video content and to promote user acquisition through computer processing, analysis, and understanding of video content. Video tennis classification has high research and application value. This article focuses on video tennis based on the selection of the basic frame of each shot and proposes an algorithm for classification of shots based on average grouping. Based on this, we use a color-coded spatial detection method to detect the type of tennis match. Then, it integrates the results of audiovisual analysis to identify and classify exciting events in tennis matches. According to statistics, although the number of people participating in tennis cannot enter the top ten, the number of spectators ranks fourth. Four tennis tournaments, masters, and crown tournaments are held every year around the world. Watching large-scale international tennis matches has become a pillar of leisure and vacation for many people. Tennis matches last from two hours to four hours or more, and there are countless large and small tennis matches around the world every year, so the number of tennis records created is staggering. And artificial intelligence technology is rarely used in tennis in the sports world (5%), but football has reached 50%. Therefore, when dealing with such a large amount of data, we urgently need to find a fast and effective video retrieval classification method to find the required information. The experiment of tennis video classification research based on machine learning technology proves that the accuracy of tennis video classification reaches 98%, so this system has high feasibility.