In this paper, we propose a system that can automatically segment a basketball video into several clips on the basis of a GOP-based scene change detection method. The length of each clip and the number of dominant color pixels of each frame are used to classify shots into close-up view, medium view, and full court view. Full court view shots are chosen to do advanced analyses such as ball tracking and parameter extracting for the transformation from a 3D real-world court to a 2D image. After that, we map points in the 2D image to the corresponding coordinates in a real-world court by some physical properties of the 3D shooting trajectory, and compute the statistics of all shooting positions. Eventually we can obtain the information about the most possible shooting positions of a professional basketball team, which is useful for opponents to adopt appropriate defense tactics.
['Ming-Chun Tien', 'Hua-Tsung Chen', 'Yi-Wen Chen', 'Ming-Ho Hsiao', 'Suh-Yin Lee']
Shot Classification of Basketball Videos and its Application in Shooting Position Extraction