Semantic Event Detection in Structured Video Using Hybrid HMM/SVM

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dc.contributor.authorBae, Tae Meon-
dc.contributor.authorKim, Cheon Seog-
dc.contributor.authorJin, Sung Ho-
dc.contributor.authorKim, Ki Hyun-
dc.contributor.authorRo, Yong Man-
dc.date.accessioned2011-03-23T01:17:01Z-
dc.date.available2011-03-23T01:17:01Z-
dc.date.issued2005-
dc.identifier.citationCIVRen
dc.identifier.urihttp://hdl.handle.net/10203/22920-
dc.description.abstractIn this paper, we propose a new semantic event detection algorithm in structured video. A hybrid method that combines HMM with SVM to detect semantic events in video is proposed. The proposed detection method has some advantages that it is suitable to the temporal structure of event thanks to Hidden Markov Models (HMM) and guarantees high classification accuracy thanks to Support Vector Machines (SVM). The performance of the proposed method is compared with that of HMM based method, which shows the performance increase in both recall and precision of semantic event detection.en
dc.language.isoen_USen
dc.publisherSpringer Verlagen
dc.titleSemantic Event Detection in Structured Video Using Hybrid HMM/SVMen
dc.typeArticleen

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