In this paper, we propose a novel framework to automatically perform player tracking and identification for sport videos filmed by a single pan-tilt-zoom camera from the court view. The proposed scheme is separated into three parts. The first part is to detect players by a deformable part model. The second part is to recognize jersey numbers by gradient differences and optical character recognition. The final part applies particle filters to track players. Experimental results demonstrate the efficacy of the proposed algorithm and the feasibility for sports video analysis.
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