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Paper Abstract and Keywords
Presentation 2024-09-06 11:05
Development and evaluation of 3D LiDAR-based anomalous behavior detection network system for station platforms
Yusuke Koyama, Hideaki Miyaji, Hiroshi Yamamoto (Ritsumeikan Univ.) IA2024-21
Abstract (in Japanese) (See Japanese page) 
(in English) Despite the various measures taken by railway companies, accidents involving people on railway tracks continue to occur.One such measure is the installation of platform doors, which is estimated to cost around 300 million yen per station, with additional costs if platform modifications are necessary.One of the accidents occurs by intoxicated passengers. It has been found that there are predictive behaviors indicating that intoxicated passengers may fall from the platform, and detecting these behaviors could potentially prevent accidents.Existing research has proposed methods for tracking individuals in public spaces using 3D depth sensors, but these methods have limitations: the detection range is narrow (up to 5 meters), and there has been no evaluation of their real-time capabilities, raising concerns about their ability to track pedestrians in real-time.Therefore, in this study, we attempt to develop a system that observe the behaviors of people on station platformby analizing 3D point cloud data taken by 3D LiDAR (Light Detection and Ranging)that can accurately measure the distance, position, and shape of the object.This system will observe the behavior of individuals on the platform and detect abnormal behaviors.
Keyword (in Japanese) (See Japanese page) 
(in English) IoT / railroad accident prevention / 3D LiDAR / 3D point cloud analysis / abnormal detection / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 182, IA2024-21, pp. 52-57, Sept. 2024.
Paper # IA2024-21 
Date of Issue 2024-08-29 (IA) 
ISSN 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)
Download PDF IA2024-21

Conference Information
Committee IA  
Conference Date 2024-09-05 - 2024-09-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet Operation and Management, Network Architecture, Communication Protocols, IoT, etc. 
Paper Information
Registration To IA 
Conference Code 2024-09-IA 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Development and evaluation of 3D LiDAR-based anomalous behavior detection network system for station platforms 
Sub Title (in English)  
Keyword(1) IoT  
Keyword(2) railroad accident prevention  
Keyword(3) 3D LiDAR  
Keyword(4) 3D point cloud analysis  
Keyword(5) abnormal detection  
Keyword(6)  
Keyword(7)  
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1st Author's Name Yusuke Koyama  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
2nd Author's Name Hideaki Miyaji  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
3rd Author's Name Hiroshi Yamamoto  
3rd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ.)
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Speaker Author-1 
Date Time 2024-09-06 11:05:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2024-21 
Volume (vol) vol.124 
Number (no) no.182 
Page pp.52-57 
#Pages 6 
Date of Issue 2024-08-29 (IA) 

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