Semantic event detection in structured video using hybrid HMM/SVM

Authors:
Tae Meon Bae;Cheon Seog Kim;Sung Ho Jin;Ki Hyun Kim;Yong Man Ro
Affiliations:
IVY Lab., Information and Communication University (ICU), Deajeon, Korea;IVY Lab., Information and Communication University (ICU), Deajeon, Korea;IVY Lab., Information and Communication University (ICU), Deajeon, Korea;IVY Lab., Information and Communication University (ICU), Deajeon, Korea;IVY Lab., Information and Communication University (ICU), Deajeon, Korea
Venue:
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Year:
2005

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Abstract

In 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.