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Paper Abstract and Keywords
Presentation 2024-06-18 10:30
Development and evaluation of congestion estimation sensing system with self-learning function using 3D LiDAR
Kenji Murase, Hideaki Miyaji, Hiroshi Yamamoto (Ritsumeikan Univ.) IA2024-9 ICSS2024-9
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, surveillance systems using security cameras have been attracting attention for security in public places.
However, since these cameras capture a lot of privacy information such as indoors pictures, the risk of information leakage is a concern.
In existing studies, a new system has been constructed to estimate the degree of congestion in each area.
This system measures the number of smartphones from the signals of Bluetooth beacons by multiple receivers installed in a room. Use this data to estimate the congestion.
However, since many people have multiple Bluetooth devices, this method causes errors in estimating congestion.
In this study, we propose a system that uses 3D LiDAR, which can accurately measure the number of people in a limited area of public space, and trains a machine learning model based on this data to accurately estimate congestion from the number of Bluetooth beacons observed.
Using this model, accurate estimation of congestion is possible even receivers only installed Bluetooth beacon.
A demonstration experiment will be conducted on the Biwako-Kusatsu campus of Ritsumeikan University to evaluate the accuracy of the machine learning model constructed by the proposed system for estimating the number of people and its processing speed,
We will clarify that the machine learning model can accurately estimate the number of people based on the processing time for analyzing point cloud data acquired from LiDAR and the number of Bluetooth beacons.
Keyword (in Japanese) (See Japanese page) 
(in English) IoT / Machine Learning / 3D LiDAR / 3D point cloud analysis / headcount estimation / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 82, IA2024-9, pp. 48-54, June 2024.
Paper # IA2024-9 
Date of Issue 2024-06-10 (IA, ICSS) 
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 IA2024-9 ICSS2024-9

Conference Information
Committee IA ICSS  
Conference Date 2024-06-17 - 2024-06-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Okayama University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet Security, etc. 
Paper Information
Registration To IA 
Conference Code 2024-06-IA-ICSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Development and evaluation of congestion estimation sensing system with self-learning function using 3D LiDAR 
Sub Title (in English)  
Keyword(1) IoT  
Keyword(2) Machine Learning  
Keyword(3) 3D LiDAR  
Keyword(4) 3D point cloud analysis  
Keyword(5) headcount estimation  
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1st Author's Name Kenji Murase  
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 2024-06-18 10:30:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2024-9, ICSS2024-9 
Volume (vol) vol.124 
Number (no) no.82(IA), no.83(ICSS) 
Page pp.48-54 
#Pages 7 
Date of Issue 2024-06-10 (IA, ICSS) 

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