Content-based sports video analysis and modeling

We propose in this paper some new methods for analyzing and modeling sports based on their low-level visual are first automatically identified by using the color features derived from its video are first automatically identified by using the color features derived from its video shots. To improve the performance on the identification of dominant scenes and reduce the dependency on a proper threshold, a comparison on different forms of shot color features, including shot color histograms, their principal components, and subspace linear discriminant representations, is performed. Second, the content compactness and motion attributes of clustered video shots are analyzed to differentiate dominant scene types. A scene transition diagram is then constructed to form a structural descriptor for sports video contents. Third, the video shots belonging to each dominant scene are processed, using customized schemes and domain specific knowledge, to identify interesting play events in the sports video. Experimental results on identification of dominant scenes, structural desprictors and high-level ball videos, are presented to demonstrate the possible applications of the proposed methods.