Paper Abstract and Keywords |
Presentation |
2015-03-19 11:00
A Study on Efficient Pedestrian Detection by Combining CNN and SVM. Takuma Saito, Yuji Waizumi, Kazuyuki Tanaka (Tohoku Univ.) BioX2014-46 PRMU2014-166 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Detecting pedestrian in real time is challenging problem for application to road safety. For high-accuracy and high-speed pedestrian detection, we propose a efficient pedestrian detection system by combining Convolutional
Neural Network(CNN) and Support Vector Machine(SVM). To compress and select the features, we use CNN which can extract compressed features from input space by its convolution operation. The compressed features include information which separates pedestrian from background image effectively due to the supervised learning nature of CNN. By sorting them out, we can obtain more compact features for pedestrian detection. Linear SVM and nonlinear SVM are used to build a hierarchical detection system. For high-speed detection in nonlinear SVM phase, the compressed features are inputted to nonlinear SVM. In detection experiment, we demonstrated that our proposed method can achieve higher detection accuracy than linear SVM and shorter computation time than nonlinear SVM. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
pedestrian detection / CNN / SVM / feature selection / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 114, no. 521, PRMU2014-166, pp. 41-46, March 2015. |
Paper # |
PRMU2014-166 |
Date of Issue |
2015-03-12 (BioX, PRMU) |
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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BioX2014-46 PRMU2014-166 |
Conference Information |
Committee |
PRMU BioX |
Conference Date |
2015-03-19 - 2015-03-20 |
Place (in Japanese) |
(See Japanese page) |
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(See Japanese page) |
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Paper Information |
Registration To |
PRMU |
Conference Code |
2015-03-PRMU-BioX |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Efficient Pedestrian Detection by Combining CNN and SVM. |
Sub Title (in English) |
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Keyword(1) |
pedestrian detection |
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CNN |
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SVM |
Keyword(4) |
feature selection |
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1st Author's Name |
Takuma Saito |
1st Author's Affiliation |
Tohoku University (Tohoku Univ.) |
2nd Author's Name |
Yuji Waizumi |
2nd Author's Affiliation |
Tohoku University (Tohoku Univ.) |
3rd Author's Name |
Kazuyuki Tanaka |
3rd Author's Affiliation |
Tohoku University (Tohoku Univ.) |
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Speaker |
Author-1 |
Date Time |
2015-03-19 11:00:00 |
Presentation Time |
30 minutes |
Registration for |
PRMU |
Paper # |
BioX2014-46, PRMU2014-166 |
Volume (vol) |
vol.114 |
Number (no) |
no.520(BioX), no.521(PRMU) |
Page |
pp.41-46 |
#Pages |
6 |
Date of Issue |
2015-03-12 (BioX, PRMU) |
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