CVEPS - a compressed video editing and parsing system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Automatic Classification of Tennis Video for High-level Content-based Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
A unified approach to shot change detection and camera motion characterization
IEEE Transactions on Circuits and Systems for Video Technology
An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis
IEEE Transactions on Circuits and Systems for Video Technology
Rapid estimation of camera motion from compressed video with application to video annotation
IEEE Transactions on Circuits and Systems for Video Technology
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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.