Abstract
This paper presents a novel approach to analyzing basketball games. It uses image processing techniques to track player movements, evaluate passes and shots, and visualize game dynamics. The system employs player and ball detection methods, leveraging appearance embedding-based particle filters for robust tracking across consecutive frames. We generate trajectory diagrams that provide insights into team strategies and player performance by applying projective transformation to map coordinates from player feet to the basketball court. Key challenges addressed include improving tracking accuracy under dynamic conditions, minimizing over-detections in pass and shot judgment, and refining ball possession calculations. Experimental results show high tracking accuracy for players, but lower performance in ball tracking and shot detection, particularly in high-speed movements or when objects are occluded. The analysis also revealed that player and team behaviors, such as passing success rates and movement patterns, could be effectively visualized through trajectory diagrams. While the current system provides valuable insights into game strategies, further improvements are needed, particularly in enhancing the reliability of tracking, judgment of passes and shots, and clarity of trajectory in dense sequences of plays.