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Paper Abstract and Keywords
Presentation 2024-11-14 10:55
Gait Recognition from Overhead View Using Transformer-Based Models
Ryusei Kawa, Takeshi Umezawa, Noritaka Osawa (Chiba Univ.) ISEC2024-64 SITE2024-61 LOIS2024-28
Abstract (in Japanese) (See Japanese page) 
(in English) In personal identification using overhead-view images, conventional methods face challenges in maintaining accuracy when clothing changes. This study proposes a gait recognition model that uses cropped images around the head-top area along with sequences of coordinate displacements. We trained and evaluated the model using a custom dataset of five subjects, each wearing 20 different outfits. Additionally, we investigated whether converting images to grayscale, thereby removing color information, could improve recognition accuracy. On test data with clothing not included in the training set, the model achieved an accuracy of 0.770 with RGB images and 0.865 with grayscale images, demonstrating that the method improved robustness against clothing changes.
Keyword (in Japanese) (See Japanese page) 
(in English) Gait Recognition / Deep Learning / Image Recognition / Ceiling Camera / Time-Series Data / Dynamic Feature Analysis / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 257, LOIS2024-28, pp. 11-16, Nov. 2024.
Paper # LOIS2024-28 
Date of Issue 2024-11-07 (ISEC, SITE, LOIS) 
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 ISEC2024-64 SITE2024-61 LOIS2024-28

Conference Information
Committee SITE LOIS ISEC  
Conference Date 2024-11-14 - 2024-11-15 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To LOIS 
Conference Code 2024-11-SITE-LOIS-ISEC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Gait Recognition from Overhead View Using Transformer-Based Models 
Sub Title (in English)  
Keyword(1) Gait Recognition  
Keyword(2) Deep Learning  
Keyword(3) Image Recognition  
Keyword(4) Ceiling Camera  
Keyword(5) Time-Series Data  
Keyword(6) Dynamic Feature Analysis  
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Keyword(8)  
1st Author's Name Ryusei Kawa  
1st Author's Affiliation Chiba University (Chiba Univ.)
2nd Author's Name Takeshi Umezawa  
2nd Author's Affiliation Chiba University (Chiba Univ.)
3rd Author's Name Noritaka Osawa  
3rd Author's Affiliation Chiba University (Chiba Univ.)
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Speaker Author-1 
Date Time 2024-11-14 10:55:00 
Presentation Time 25 minutes 
Registration for LOIS 
Paper # ISEC2024-64, SITE2024-61, LOIS2024-28 
Volume (vol) vol.124 
Number (no) no.255(ISEC), no.256(SITE), no.257(LOIS) 
Page pp.11-16 
#Pages 6 
Date of Issue 2024-11-07 (ISEC, SITE, LOIS) 

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