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| Presentation | 2020-12-15 13:50 Classification of pedestrians existing in visible or blind areas using Doppler radar -- An Approach Using Long Short-Term Memory -- Sora Hayashi, Daiki Isobe (Ritsumeikan Univ.), Kenshi saho (Toyama Pref. Univ.), Masao Masugi (Ritsumeikan Univ.)  WBS2020-29 ITS2020-25 RCC2020-32 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this report, the walking positions of pedestrians, whether they are directly visible from the observation point or in the blind spot areas such as behind a wall, were evaluated by the Doppler radar. An LSTM (Long Short-Term Memory) model, which is a kind of deep learning neural network, was used in our study. Time-series data of the gait velocity, which correspond to the legs and torso of pedestrians, and time transition of received power were input to LSTM. Experimental results confirmed that our method is effective for estimating the positions of pedestrians and classifying their gait patterns in each area. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Doppler Radar / Gait Analysis / Monitoring of Blind Area / Deep Learning / Long Short-Term Memory / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 291, ITS2020-25, pp. 121-125, Dec. 2020. | |
| Paper # | ITS2020-25 | |
| Date of Issue | 2020-12-07 (WBS, ITS, RCC) | |
| 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 | WBS2020-29 ITS2020-25 RCC2020-32 |
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| Conference Information | |
| Committee | ITS WBS RCC |
| Conference Date | 2020-12-14 - 2020-12-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | ITS Communications, Reliable Communication and Control, Radar and Sensing, etc. |
| Paper Information | |
| Registration To | ITS |
| Conference Code | 2020-12-ITS-WBS-RCC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Classification of pedestrians existing in visible or blind areas using Doppler radar |
| Sub Title (in English) | An Approach Using Long Short-Term Memory |
| Keyword(1) | Doppler Radar |
| Keyword(2) | Gait Analysis |
| Keyword(3) | Monitoring of Blind Area |
| Keyword(4) | Deep Learning |
| Keyword(5) | Long Short-Term Memory |
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| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Sora Hayashi |
| 1st Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 2nd Author's Name | Daiki Isobe |
| 2nd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 3rd Author's Name | Kenshi saho |
| 3rd Author's Affiliation | Toyama Prefectural University (Toyama Pref. Univ.) |
| 4th Author's Name | Masao Masugi |
| 4th Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2020-12-15 13:50:00 |
| Presentation Time | 25 minutes |
| Registration for | ITS |
| Paper # | WBS2020-29, ITS2020-25, RCC2020-32 |
| Volume (vol) | vol.120 |
| Number (no) | no.290(WBS), no.291(ITS), no.292(RCC) |
| Page | pp.121-125 |
| #Pages | 5 |
| Date of Issue | 2020-12-07 (WBS, ITS, RCC) |
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