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Paper Abstract and Keywords
Presentation 2020-05-29 15:10
Distant pedestrian detection from nighttime NIR video by moving-region zooming
Atsuki Hiramatsu, Yusuke Kameda (TUS), Hiroshi Ikeoka (Fukuyama Univ.), Takayuki Hamamoto (TUS) SIP2020-16 BioX2020-16 IE2020-16 MI2020-16
Abstract (in Japanese) (See Japanese page) 
(in English) Highly accurate pedestrian detection technology is required for functions such as automatic emergency braking, which are important in preventing traffic accidents and reducing damage. Visible light cameras are generally used for pedestrian detection. However, there is a problem that clear images cannot be captured in low-light environments such as at night. On the other hand, the combination of near-infrared (NIR) camera and near-infrared light can capture clear images even in low-light environments. Thus it is expected to develop the pedestrian detection technology using them. Previous studies on pedestrian detection from NIR images include methods using deep convolutional neural networks (CNN) with real-time computation and high detection accuracy. However, it is difficult to detect distant pedestrians in small areas of the image because convolution and pooling are combined in the previous study CNN. In this paper, we propose a method to equalize the apparent size of distant pedestrians as preprocessing of CNN input. Specifically, it extracts the time-varying pixels from the input consecutive frames and adaptively chooses the pixel blocks to be enlarged. We aim to improve the accuracy of pedestrian detection in the postprocessing by inputting the enlarged image to the CNN.
Keyword (in Japanese) (See Japanese page) 
(in English) vehicle-mounted camera / pedestrian detection / near-infrared image / CNN / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 39, IE2020-16, pp. 79-83, May 2020.
Paper # IE2020-16 
Date of Issue 2020-05-21 (SIP, BioX, IE, MI) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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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 SIP2020-16 BioX2020-16 IE2020-16 MI2020-16

Conference Information
Committee MI IE SIP BioX ITE-IST ITE-ME  
Conference Date 2020-05-28 - 2020-05-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image and signal processing/analysis/AI technology, and their application 
Paper Information
Registration To IE 
Conference Code 2020-05-MI-IE-SIP-BioX-IST-ME 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Distant pedestrian detection from nighttime NIR video by moving-region zooming 
Sub Title (in English)  
Keyword(1) vehicle-mounted camera  
Keyword(2) pedestrian detection  
Keyword(3) near-infrared image  
Keyword(4) CNN  
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1st Author's Name Atsuki Hiramatsu  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Yusuke Kameda  
2nd Author's Affiliation Tokyo University of Science (TUS)
3rd Author's Name Hiroshi Ikeoka  
3rd Author's Affiliation Fukuyama University (Fukuyama Univ.)
4th Author's Name Takayuki Hamamoto  
4th Author's Affiliation Tokyo University of Science (TUS)
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Speaker
Date Time 2020-05-29 15:10:00 
Presentation Time 20 
Registration for IE 
Paper # IEICE-SIP2020-16,IEICE-BioX2020-16,IEICE-IE2020-16,IEICE-MI2020-16 
Volume (vol) IEICE-120 
Number (no) no.38(SIP), no.37(BioX), no.39(IE), no.40(MI) 
Page pp.79-83 
#Pages IEICE-5 
Date of Issue IEICE-SIP-2020-05-21,IEICE-BioX-2020-05-21,IEICE-IE-2020-05-21,IEICE-MI-2020-05-21 


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