Event detection in baseball video using superimposed caption recognition
Proceedings of the tenth ACM international conference on Multimedia
A mid-level representation framework for semantic sports video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Semantic Event Detection using Conditional Random Fields
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Generic play-break event detection for summarization and hierarchical sports video analysis
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Hidden Conditional Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Event tactic analysis based on broadcast sports video
IEEE Transactions on Multimedia
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
A unified framework for semantic shot classification in sports video
IEEE Transactions on Multimedia
Semantic analysis of soccer video using dynamic Bayesian network
IEEE Transactions on Multimedia
An HMM-based framework for video semantic analysis
IEEE Transactions on Circuits and Systems for Video Technology
HMM based soccer video event detection using enhanced mid-level semantic
Multimedia Tools and Applications
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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Highlight event detection is a fundamental step of semantic based video retrieval and personalized sports video browsing. In this paper, an effective hidden conditional random fields (HCRFs) based soccer video event detection method is proposed. Firstly, soccer video is classified into clips with middle level semantics. The middle level semantics are further refined into more meaningful categories in terms of camera motion information. Then the continuous soccer video sequence is classified into sequential event clips based on the transitions of middle level semantics. HCRFs are utilized to model the four highlight events (goal, shoot, foul, and placed kick) and a normal kick. Comparisons are made with the dynamic Bayesian networks (DBN) and conditional random fields (CRF) based event detection approaches. Experimental results show the effectiveness of the proposed method.