Paper Abstract and Keywords |
Presentation |
2009-05-28 16:15
Real-time estimation of human visual attention with MCMC-based particle filter Kouji Miyazato (NTT/Okinawa National College of Tech), Akisato Kimura (NTT), Shigeru Takagi (Okinawa National College of Tech), Junji Yamato (NTT) IE2009-25 PRMU2009-16 MI2009-16 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
This report proposes a new method for achieving a precise estimation of human visual attention with considerably less execution time. The main contribution of this report is the incorporation of a particle filter with Markov chain Monte-Carlo (MCMC) sampling into a previously proposed stochastic model of saliency-based human visual attention. This enables us to introduce stream processing with such as graphics processing units (GPU) for the acceleration of the estmation. Experimental results indicate that the proposed method can estimate human visual attention more than 10 times faster than and as precisely as previous methods. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
saliency / dynamic Bayesian network / stream processing / Markov chain Monte-Carlo (MCMC) / particle filter / / / |
Reference Info. |
IEICE Tech. Rep., vol. 109, no. 64, PRMU2009-16, pp. 83-88, May 2009. |
Paper # |
PRMU2009-16 |
Date of Issue |
2009-05-21 (IE, PRMU, MI) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
Download PDF |
IE2009-25 PRMU2009-16 MI2009-16 |
|