Robust camera calibration and player tracking in broadcast basketball video

With the growth of fandom population, a considerable amount of broadcast sports videos have been recorded, and a lot of research has focused on automatically detecting semantic events in the recorded video to develop an efficient video browsing tool for a general viewer. However, a professional sportsman or coach wonders about high level semantics in a different perspective, such as the offensive or defensive strategy performed by the players. Analyzing tactics is much more challenging in a broadcast basketball video than in other kinds of sports videos due to its complicated scenes and varied camera movements. In this paper, by developing a quadrangle candidate generation algorithm and refining the model fitting score, we ameliorate the court-based camera calibration technique to be applicable to broadcast basketball videos. Player trajectories are extracted from the video by a CamShift-based tracking method and mapped to the real-world court coordinates according to the calibrated results. The player position/trajectory information in the court coordinates can be further analyzed for professional-oriented applications such as detecting wide open event, retrieving target video clips based on trajectories, and inferring implicit/explicit tactics. Experimental results show the robustness of the proposed calibration and tracking algorithms, and three practicable applications are introduced to address the applicability of our system.