Research on Sports Video Image Based on Clustering Extraction

Today, with the continuous sports events, the major sports events are also loved by the majority of the audience, so the analysis of the video data of the games has higher research value and application value. This paper takes the video of volleyball, tennis, baseball, and water polo as the research background and analyses the video images of these four sports events. Firstly, image graying, image denoising, and image binarization are used to preprocess the images of the four sports events. Secondly, feature points are used to detect the four sports events. According to the characteristics of these four sports events, SIFT algorithm is adopted to detect the good performance of SIFT feature points in feature matching. According to the simulation experiment, it can be seen that the SIFT algorithm can effectively detect football and have good anti-interference. For sports recognition, this document adopts the frame cross-sectional cumulative algorithm. Through simulation experiments, it can be seen that the grouping algorithm can achieve a recognition rate of more than 80% for sporting events, so it can be seen that the recognition algorithm is suitable for recognizing sports events videos.