Paper Abstract and Keywords |
Presentation |
2010-06-10 15:50
A Study on Efficient Pedestrian Recognition using Markov Chain Monte Carlo Jaehoon Yu (Osaka Univ.), Hiroki Sugano, Ryusuke Miyamoto (NAIST), Takao Onoye (Osaka Univ.) SIS2010-12 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
For object detection including pedestrian detection, sliding window approach is widely used, in which classification using feature vectors extracted from detection windows is performed in a raster scan order. Sliding window approach requires the huge computational cost because enormous sub-windows are extracted to detect target objects that have a large variety of sizes and locations. On computationally limited embedded applications, the number of sub-windows used for detection must be reduced to achieve real-time processing. The most popular approach for embedded systems is pixel skipping that may degrade the detection accuracy. In this paper, we propose an effective pedestrian detection scheme using Markov Chain Monte Carlo (MCMC) that is expected to reduce the computational amount without degradation of the detection accuracy. Experimental results show that the proposed scheme is more accurate than the conventional scheme even though the proposed scheme used only $60.14\%$ samples, and $62.47\%$ computation time comparedto the conventional scheme respectively. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Pedestrian detection / Sliding-window approach / Markov Chain Monte Carlo / Metropolis-Hastings algorithm / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 110, no. 74, SIS2010-12, pp. 65-70, June 2010. |
Paper # |
SIS2010-12 |
Date of Issue |
2010-06-03 (SIS) |
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) |
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SIS2010-12 |
Conference Information |
Committee |
SIS |
Conference Date |
2010-06-10 - 2010-06-11 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Abashiri Public Auditorium |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
SIS |
Conference Code |
2010-06-SIS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Efficient Pedestrian Recognition using Markov Chain Monte Carlo |
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Keyword(1) |
Pedestrian detection |
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Sliding-window approach |
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Markov Chain Monte Carlo |
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Metropolis-Hastings algorithm |
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1st Author's Name |
Jaehoon Yu |
1st Author's Affiliation |
Osaka University (Osaka Univ.) |
2nd Author's Name |
Hiroki Sugano |
2nd Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
3rd Author's Name |
Ryusuke Miyamoto |
3rd Author's Affiliation |
Nara Institute of Science and Technology (NAIST) |
4th Author's Name |
Takao Onoye |
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Osaka University (Osaka Univ.) |
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Speaker |
Author-1 |
Date Time |
2010-06-10 15:50:00 |
Presentation Time |
20 minutes |
Registration for |
SIS |
Paper # |
SIS2010-12 |
Volume (vol) |
vol.110 |
Number (no) |
no.74 |
Page |
pp.65-70 |
#Pages |
6 |
Date of Issue |
2010-06-03 (SIS) |
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