Presentation 2008-03-13
Learning and Inference of Probabilistic Causal Structure Model for Indoor Behavior Prediction of Infants
Satoshi KAWATA, Yoichi MOTOMURA, Yoshifumi NISHIDA, Shouzou ISHIKAWA, Kazuyuki TANAKA,
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Abstract(in English) In order to contribute to saving children exposed to daily dangers, monitoring children's everyday behavior is essential for the paradigms of infant risk prevention. In this research, we introduce a statistical lerning to predict infant behavioral models. With the rapid progress in computer-aided processing, we have come to make use of precise graphical models based on calculation using Bayesian networks. Utilizing diverse graphical models will lead us to cover various prior knowledge dwelling in the real world. In this paper, we model probabilistic causal structure chronologically making use of diverse dynamic images and ultrasound location sensor data based on Bayesian Network. Compared with the maximum likelihood estimator dealing with machine learning, this model will give us broader perspective to predict expected human behavior more precisely in daily life.
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Keyword(in English) Bayesian Networks / Bayes Estimation / Behavior Recognition / Image Processing
Paper # NC2007-159
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Committee NC
Conference Date 2008/3/5(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Learning and Inference of Probabilistic Causal Structure Model for Indoor Behavior Prediction of Infants
Sub Title (in English)
Keyword(1) Bayesian Networks
Keyword(2) Bayes Estimation
Keyword(3) Behavior Recognition
Keyword(4) Image Processing
1st Author's Name Satoshi KAWATA
1st Author's Affiliation Tohoku Univ.()
2nd Author's Name Yoichi MOTOMURA
2nd Author's Affiliation AIST
3rd Author's Name Yoshifumi NISHIDA
3rd Author's Affiliation AIST
4th Author's Name Shouzou ISHIKAWA
4th Author's Affiliation Metropolitan College of Industrial Technology
5th Author's Name Kazuyuki TANAKA
5th Author's Affiliation Tohoku Univ.
Date 2008-03-13
Paper # NC2007-159
Volume (vol) vol.107
Number (no) 542
Page pp.pp.-
#Pages 4
Date of Issue