Paper Abstract and Keywords |
Presentation |
2017-03-07 13:00
Deep Learning Approach for Moving Object Tracking using Microwave Doppler Signals Motoko Tachibana, Michiyo Hiramoto, Kurato Maeno (OKI) ITS2016-82 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In this report, we present a study on tracking using Microwave Doppler Sensor adopting deep learning approach.
Measurement of moving object by Microwave Doppler Sensor is more stable than by camera, because it is little susceptible for environment factor, such as weather or lighting. For the purpose, firstly we have to track moving objects which are detected at every sampling time. However, under the condition which two or more objects move at similar speed and are at near positions it is difficult to distinguish them. Therefore, we have adopted deep learning approach for tracking objects in order to solve these difficulties. Concretely, we have created deep convolutional network model for comparison procedure which evaluate whether one of the observed value corresponds with one of the observed value history coordinated by objects. As a result, we achieved high accuracy for the comparison. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Microwave Doppler Sensor / Tracking / Deep Learning / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 502, ITS2016-82, pp. 31-35, March 2017. |
Paper # |
ITS2016-82 |
Date of Issue |
2017-02-28 (ITS) |
ISSN |
Print edition: ISSN 0913-5685 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) |
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ITS2016-82 |
Conference Information |
Committee |
ITS IEE-ITS |
Conference Date |
2017-03-07 - 2017-03-07 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Kyoto Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Information Processing for ITS, etc. |
Paper Information |
Registration To |
ITS |
Conference Code |
2017-03-ITS-ITS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Deep Learning Approach for Moving Object Tracking using Microwave Doppler Signals |
Sub Title (in English) |
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Microwave Doppler Sensor |
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Tracking |
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Deep Learning |
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1st Author's Name |
Motoko Tachibana |
1st Author's Affiliation |
Oki Electric Industry Co., Ltd. (OKI) |
2nd Author's Name |
Michiyo Hiramoto |
2nd Author's Affiliation |
Oki Electric Industry Co., Ltd. (OKI) |
3rd Author's Name |
Kurato Maeno |
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Oki Electric Industry Co., Ltd. (OKI) |
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Speaker |
Author-1 |
Date Time |
2017-03-07 13:00:00 |
Presentation Time |
20 minutes |
Registration for |
ITS |
Paper # |
ITS2016-82 |
Volume (vol) |
vol.116 |
Number (no) |
no.502 |
Page |
pp.31-35 |
#Pages |
5 |
Date of Issue |
2017-02-28 (ITS) |
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