Research for Foul Gesture Recognition of Basketball Referees Based on Skeletal Data

Abstract

In recent years, artificial intelligence (AI) has found applications in various fields, including sports. Technology that uses machine learning to analyze camera images and provide real-time information has attracted particular attention. In basketball, table officials (TO) work with referees to assist with the game. They are responsible for tasks such as recording the score, managing fouls, and operating the timer. Currently, all of these tasks are performed manually, requiring at least four TO for an official game. Interruptions sometimes occur due to human error. In this research, we developed an AI-based system that recognizes foul gestures of basketball referees to assist TO with their duties. Using videos in which the referee was clearly visible, we extracted skeletal information with MMPose and classified foul gestures based on time-series data. The system achieved 95% accuracy in classifying the type of foul and 74.4% accuracy in recognizing the jersey numbers of the players who committed the foul. This research is expected to support TO tasks and ensure fair game management. However, further improvements, especially in jersey number recognition accuracy, are necessary for practical implementation.