Presentation 2006-03-17
Lecture Context Recognition based on Statistical Feature of Lecturer Action for Automatic Video Recording
Yoshitaka SUGIMOTO, Takafumi MARUTANI, Koh KAKUSHO, Michihiko MINOH,
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Abstract(in English) In this paper, we discuss the problem of recognizing situations of lectures for shooting lecture videos automatically. We classify the situations of the lectures based on the subjects to be gazed at for understanding the lectures in a lecture room. However it is not easy to recognize the lecture situations by conventional approaches based on the features of the sensory data obtained in a lecture room because the same sensory data occurs in the different lecture situations. We employ statistical properties for the occurrence of sensory data in each lecture situation together with the transition probability between lecture situations. This process is realized by Hidden Markov Model that represents those statistical properties.
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Keyword(in English) Automatic shooting video / Recognizing lecture situation / Statistical properties / Hidden Markov Model
Paper # PRMU2005-260
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Committee PRMU
Conference Date 2006/3/10(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Lecture Context Recognition based on Statistical Feature of Lecturer Action for Automatic Video Recording
Sub Title (in English)
Keyword(1) Automatic shooting video
Keyword(2) Recognizing lecture situation
Keyword(3) Statistical properties
Keyword(4) Hidden Markov Model
1st Author's Name Yoshitaka SUGIMOTO
1st Author's Affiliation Graduate School of Informatics, Kyoto Univ.()
2nd Author's Name Takafumi MARUTANI
2nd Author's Affiliation Graduate School of Informatics, Kyoto Univ.
3rd Author's Name Koh KAKUSHO
3rd Author's Affiliation Academic Center for Computing and Media Studies, Kyoto Univ.
4th Author's Name Michihiko MINOH
4th Author's Affiliation Academic Center for Computing and Media Studies, Kyoto Univ.
Date 2006-03-17
Paper # PRMU2005-260
Volume (vol) vol.105
Number (no) 674
Page pp.pp.-
#Pages 8
Date of Issue