Accumulated motion energy fields estimation and representation for semantic event detection
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Journal of Visual Communication and Image Representation
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Statistical motion information extraction and representation for semantic video analysis
IEEE Transactions on Circuits and Systems for Video Technology
Semantic concept mining in cricket videos for automated highlight generation
Multimedia Tools and Applications
A review of vision-based systems for soccer video analysis
Pattern Recognition
Soccer video event detection by fusing middle level visual semantics of an event clip
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Bayesian belief network based broadcast sports video indexing
Multimedia Tools and Applications
Finding the game flow from sports video
J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
HMM-based ball hitting event exploration system for broadcast baseball video
Journal of Visual Communication and Image Representation
Recognizing tactic patterns in broadcast basketball video using player trajectory
Journal of Visual Communication and Image Representation
HMM based soccer video event detection using enhanced mid-level semantic
Multimedia Tools and Applications
Multimedia Tools and Applications
Sports Information Retrieval for Video Annotation
International Journal of Digital Library Systems
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This paper aims to extract baseball game highlights based on audio-motion integrated cues. In order to better describe different audio and motion characteristics in baseball game highlights, we propose a novel representation method based on likelihood models. The proposed likelihood models measure the "likeliness" of low-level audio features and motion features to a set of predefined audio types and motion categories, respectively. Our experiments show that using the proposed likelihood representation is more robust than using low-level audio/motion features to extract the highlight. With the proposed likelihood models, we then construct an integrated feature representation by symmetrically fusing the audio and motion likelihood models. Finally, we employ a hidden Markov model (HMM) to model and detect the transition of the integrated representation for highlight segments. A series of experiments have been conducted on a 12-h video database to demonstrate the effectiveness of our proposed method and show that the proposed framework achieves promising results over a variety of baseball game sequences.