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Automatic basketball tracking in broadcast basketball video

University of British Columbia

We proposed and implemented an automatic basketball detection and tracking system for broadcast basketball video recorded with a single pan-tilt-zoom camera, using knowledge of player tracking information. The task is challenging because the basketball is blurred due to the camera and the ball's fast movements, and broadcast video compression; also the motion pattern of the basketball is complicated and the ball is hard to distinguish from the cluttered background region. We incorporated three independent detection approaches to detect the basketball and tracked the basketball using the Kalman Filter, and then we analyzed the tracklets and selected the passing / shooting tracklets and inferred the player possession information. We tested the system using 830 frames in broadcast basketball video, and our system demonstrated the ability to track some passing / shooting actions and then infer the player who controls the ball. The system is a first attempt to extend the intelligent basketball tracking system to include basketball tracking and player possession inference. Our proposed methodologies can be extended to other intelligent sports analysis systems, even when the ball movement in the sport is not constrained in two dimensional space.

Text

2012-08-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/43072

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/