Artificial Intelligence-based Volleyball Target Detection and Behavior Recognition Method

Abstract

Volleyball has limitations in relying on judges’ subjective judgments alone to call penalties for infractions in the court. While video detail enhancement technology is extremely useful for target tracking and extraction in sports video, the current research on video detail enhancement technology does not pay much attention to the development of ball game violation tracking and recognition. Therefore, the study uses the fusion algorithm of wavelet exchange method and three-frame difference method and background subtraction method to detect and extract the motion targets, and uses the improved CamShift tracking algorithm and HMM to track and identify the tracking targets for the violation actions. Comprehensively, the study constructs a tracking recognition model for volleyball violation based on video enhancement technology to achieve accurate penalty in intense rivalry games. Through experimental analysis and comparison, the tracking F-measure value of the model constructed by the study is 0.89, which can achieve a good tracking effect, the recognition accuracy is 99.76%, and the average error is 0.003, which can effectively realize the tracking recognition of players’ illegal actions during volleyball, and objectively make court penalties to guarantee the fairness and justice of the game.