著者

EL HAMDI Younes Okamoto Takumi CHONG Nak Young SUH Il Hong

出版者

人工知能学会

雑誌

人工知能学会全国大会論文集 (ISSN:13479881)

巻号頁・発行日

vol.26, 2012

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In this paper, we study the problem of creating an inference mechanism to recognize and respond to human behavior. We provide probabilistic methods to build a new Bayesian framework to deal with human tracking problem. Specifically, we present a set of efficient algorithms that encompass the learning solutions for practical applications which cope with unreliable and noisy measurements. Unlike almost all of related works, we propose an efficient algorithm for sensing systems that presents an alternative to sensors that are sometimes perceived as invasive, where notably we do not use vision-based learning. Preliminary results show that the proposed system can be deployed in different environments and significantly outperforms existing methods in a very reliable manner.