Proposal of an Identification Method for Over-the-Table Stroke Types in Table Tennis

This paper proposes an automated method for identifying over-the-table stroke types in table tennis using image processing. In table tennis, stroke types such as high-speed type (chiquita, flick), stop type (stop), and deep return type (push, flow, etc.) performed near the table are crucial for gaining advantage, yet their analysis is time-consuming and often subjective. Our proposed method employs YOLOv8 to detect players, table, and ball from standard match footage, and then identifies bounce points to distinguish over-the-table techniques from other strokes. The identification algorithm analyzes ball trajectory, bounce positions, and stroke speed to categorize stroke types, with machine learning methods achieving superior performance. Additionally, the method classifies forehand and backhand drives by analyzing player posture at the moment of striking. Experiments on World Table Tennis match videos showed high accuracy: 99.6% for table detection, 96.1% for ball detection, and 89.9% for bounce point detection. The identification of over-the-table stroke types achieved 85.1% accuracy using machine learning methods, while forehand and backhand drives were correctly classified at 90.2% accuracy using rule-based methods. This automated system provides coaches and players with an objective and efficient tool for analyzing technical performance in table tennis.