Discovering similar patterns in time series
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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)
Real-Time Tracking for Enhanced Tennis Broadcasts
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A probabilistic template-based approach to discovering repetitive patterns in broadcast videos
Proceedings of the 13th annual ACM international conference on Multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Trajectory based event tactics analysis in broadcast sports video
Proceedings of the 15th international conference on Multimedia
Event tactic analysis based on player and ball trajectory in broadcast video
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Event tactic analysis based on broadcast sports video
IEEE Transactions on Multimedia
Discovering tactics in broadcast sports video with trajectories
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Multimedia Tools and Applications
Bayesian belief network based broadcast sports video indexing
Multimedia Tools and Applications
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Tennis game is a mutual and interactive process between the two opponents, and audiences are usually interested in how the players oppose and compete with each other. However, previous works on tennis video indexing are mainly based on analyses of player’s behavior, such as movement and gesture, and seldom provide information of the match process. In this paper, a novel approach is proposed for tennis video indexing by mining the salient patterns in the match process. The match is first characterized by a time sequence of players’ joint movements, based on which an unsupervised way is performed to extract the sub-sequences with high occurrence frequencies. These sub-sequences, called patterns, reflect the technical styles and tactics of the players, and offer an effective manner to organize the video. Evaluations on four hour broadcasting tennis videos show very promising results.