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Paper Abstract and Keywords
Presentation 2024-01-18 13:30
Enhancing Human Skeleton Estimation with Multi-Frame mmWave Radar Point Cloud-based Method
Xintong Shi, Tomoaki Ohtsuki (Keio Univ.) SeMI2023-52
Abstract (in Japanese) (See Japanese page) 
(in English) Millimeter-Wave (mmWave) radar-based skeleton estimation has emerged as a focal point in the realm of human motion analysis and sensing, offering distinct advantages over conventional RGB camera, depth camera, and inertial sensor approaches. One notable advantage is its ability to operate independently of varying lighting conditions. Furthermore, it successfully addresses limitations inherent in depth camera methods, such as susceptibility to reflections and occlusions, and excels in providing precise real-time tracking, thereby elevating its overall performance.
However, prevalent human skeleton estimation methods utilizing point cloud data often rely on single-frame inputs or incorporate voxelization, introducing associated drawbacks. This research presents an innovative model based on a combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM) architecture specifically designed for handling multi-frame point cloud data without resorting to voxelization. Additionally, a Long Short-Term Memory (LSTM)-based neural network is introduced to evaluate the reliability of the point cloud, thereby enhancing overall robustness.
Experimental results underscore the superior accuracy and robustness achieved in human skeleton estimation when compared to conventional single-frame methods. This research contributes to advancing the efficacy of mmWave radar-based skeletal tracking systems, paving the way for enhanced applications in human motion analysis and sensing.
Keyword (in Japanese) (See Japanese page) 
(in English) Millimeter-Wave radar / Human skeleton estimation / Convolutional neural network / Bi-directional long short-term memory neural network / Point cloud / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 345, SeMI2023-52, pp. 18-21, Jan. 2024.
Paper # SeMI2023-52 
Date of Issue 2024-01-11 (SeMI) 
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 SeMI2023-52

Conference Information
Committee SeMI  
Conference Date 2024-01-18 - 2024-01-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Raki House Kaiji 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SeMI 
Conference Code 2024-01-SeMI 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Enhancing Human Skeleton Estimation with Multi-Frame mmWave Radar Point Cloud-based Method 
Sub Title (in English)  
Keyword(1) Millimeter-Wave radar  
Keyword(2) Human skeleton estimation  
Keyword(3) Convolutional neural network  
Keyword(4) Bi-directional long short-term memory neural network  
Keyword(5) Point cloud  
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1st Author's Name Xintong Shi  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Tomoaki Ohtsuki  
2nd Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2024-01-18 13:30:00 
Presentation Time 20 minutes 
Registration for SeMI 
Paper # SeMI2023-52 
Volume (vol) vol.123 
Number (no) no.345 
Page pp.18-21 
#Pages 4 
Date of Issue 2024-01-11 (SeMI) 

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