Abstract
In this paper, we present a novel approach for tennis video analysis, which can automatically classify video shots into 5 classes based on MPEG motion vectors and other features. Two types of features have been used: domain-independent features, such as the local motion activity and the persistent camera pan motion, and domain-dependent, such as the motion activity ratio in the court model. Combining these low-level features with domain knowledge of the tennis game, we can categorize the tennis video shots into five classes, which cover majority of the live tennis video shots, and derive semantic annotation for all shot classes. The results can be used in the higher-level video analysis, including structure analysis, table of content extraction for sports video, video summary and personalization. The proposed approach can easily be extended to analyzing other sports.
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© 2002 Springer-Verlag Berlin Heidelberg
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Yu, Xd., Duan, Ly., Tian, Q. (2002). Shot Classification of Sports Video Based on Features in Motion Vector Field. In: Chen, YC., Chang, LW., Hsu, CT. (eds) Advances in Multimedia Information Processing — PCM 2002. PCM 2002. Lecture Notes in Computer Science, vol 2532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36228-2_32
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DOI: https://doi.org/10.1007/3-540-36228-2_32
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