06月14日(Thu) 09:00〜12:20 P会場(-ふるさと伝承センターみやび館／和室(1軒屋(50坪)))
| 演題番号 | 3P1-IOS-2a-1 |
|---|---|
| 題目 | Developing a Bayesian framework for human behavior tracking |
| 著者 | EL HAMDI Younes(JAIST) Okamoto Takumi(JAIST) CHONG Nak Young(JAIST) SUH Il Hong(Hanyang University) |
| 時間 | 06月14日(Thu) 09:00〜09:30 |
| 概要 | 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. |
| 論文 | PDFファイル |