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Paper Abstract and Keywords
Presentation 2017-10-13 15:50
A Study on Traffic Sign Detection and Classification with Single Shot Detection
Janet Mardjuki (Simon Fraser Univ.), Yongqing Sun, Shingo Ando, Kinebuchi Tetsuya (NTT) PRMU2017-96
Abstract (in Japanese) (See Japanese page) 
(in English) For this paper, we would be presenting our basic investigation on real life traffic-sign detection and classification in the form of images. We applied one of the latest states of art method in object detection on the public dataset that was submitted to Conference on Computer Vision and Pattern Recognition (CVPR) in 2016. The dataset we would be working was submitted as part of the paper "Traffic-Sign Detection and Classification in the Wild". Unlike various dataset that was available in the past, this traffic sign dataset could represent the images encountered in real life. For their own experiment, they used RCNN to detect and classify the traffic sign, which is not the latest and fastest method that is currently available. Thus, we decided to conduct some experiments with the dataset using different method, Single Shot Multi Detector, which is one of the most accurate and fastest methods that are currently available. In this paper, we will describe our observation on several experiment conducted to optimize the detection speed and the accuracy for this particular dataset.
Keyword (in Japanese) (See Japanese page) 
(in English) Object detection / SSD / Deep learning / Traffic sign detection / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 238, PRMU2017-96, pp. 187-191, Oct. 2017.
Paper # PRMU2017-96 
Date of Issue 2017-10-05 (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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Conference Information
Committee PRMU  
Conference Date 2017-10-12 - 2017-10-13 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2017-10-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Traffic Sign Detection and Classification with Single Shot Detection 
Sub Title (in English)  
Keyword(1) Object detection  
Keyword(2) SSD  
Keyword(3) Deep learning  
Keyword(4) Traffic sign detection  
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1st Author's Name Janet Mardjuki  
1st Author's Affiliation Simon Fraser University, Canada (Simon Fraser Univ.)
2nd Author's Name Yongqing Sun  
2nd Author's Affiliation NTT Media Intelligence Laboratories (NTT)
3rd Author's Name Shingo Ando  
3rd Author's Affiliation NTT Media Intelligence Laboratories (NTT)
4th Author's Name Kinebuchi Tetsuya  
4th Author's Affiliation NTT Media Intelligence Laboratories (NTT)
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Speaker Author-1 
Date Time 2017-10-13 15:50:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2017-96 
Volume (vol) vol.117 
Number (no) no.238 
Page pp.187-191 
#Pages 5 
Date of Issue 2017-10-05 (PRMU) 

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