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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
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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 BioX2014-46 PRMU2014-166

Conference Information
Committee PRMU BioX  
Conference Date 2015-03-19 - 2015-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (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)  
Keyword(1) pedestrian detection  
Keyword(2) CNN  
Keyword(3) 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
Date of Issue 2015-03-12 (BioX, PRMU) 


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