Modeling spatiotemporal relationships between moving objects for event tactics analysis in tennis videos
Evolution of spatial relationships between objects often provides important clues for semantic video analysis. We present a symbolic representation that describes spatiotemporal characteristics and facilitates tactics detection based on string matching. To find typical spatiotemporal patterns of a targeted tactic, we organize training sequences as a tree, and effectively discover frequent patterns from the structure. Tactics detection is conducted by comparing a given test sequence with these frequent patterns. To realize the proposed idea, we develop elaborate audio/video processes to transform broadcasting tennis videos into symbolic sequences, and comprehensively tackle event detection and tactics analysis. We experiment on ten most important tennis championships in the year 2008, and report promising detection results on seven events/tactics. We demonstrate not only the effectiveness of the proposed methods, but also study the impacts brought by the results of tactics analysis.
tactics analysis event detection spatiotemporal modeling tennis video analysis