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Paper Abstract and Keywords
Presentation 2021-08-26 15:30
Pedestrian Position Estimation by Integrating AlphaPose Based Human Pose Estimation and PredRNN++ Based Spatiotemporal Series Prediction for Autonomous Driving Systems
Zhaowei Zhang (Waseda U.), Eiji Fukuzawa (Waseda U./Yazaki Corp.), Jun Ohya (Waseda U.) PRMU2021-12
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, with the development of automatic driving technology, more experimental automatic driving vehicles have been developed, where it is inevitable to solve the problem of security. In automatic driving systems, it is an important research topic to predict how the accidence will occur in the future. The key of the problem is to predict the position of pedestrians in front of the vehicle. Pedestrian position prediction under the risk prediction branch is the issue to be solved in this research. By comparing several related studies of pedestrian position prediction, this paper explores the combination of 2D background stripping modeling and spatiotemporal sequence. More specifically, a pedestrian position prediction method based on AlphaPose human pose estimation and PredRNN++ is proposed, and a data preprocessing method which can improve the test accuracy is also proposed. The validity of the proposed method is experimentally shown.
Keyword (in Japanese) (See Japanese page) 
(in English) Autonomous driving / Pedestrians Position Prediction / Human Pose Estimation / Spatiotemporal Prediction / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 155, PRMU2021-12, pp. 30-35, Aug. 2021.
Paper # PRMU2021-12 
Date of Issue 2021-08-19 (PRMU) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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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Conference Information
Committee PRMU  
Conference Date 2021-08-26 - 2021-08-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) CV/PR techniques for human-robot cooperation 
Paper Information
Registration To PRMU 
Conference Code 2021-08-PRMU 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Pedestrian Position Estimation by Integrating AlphaPose Based Human Pose Estimation and PredRNN++ Based Spatiotemporal Series Prediction for Autonomous Driving Systems 
Sub Title (in English)  
Keyword(1) Autonomous driving  
Keyword(2) Pedestrians Position Prediction  
Keyword(3) Human Pose Estimation  
Keyword(4) Spatiotemporal Prediction  
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1st Author's Name Zhaowei Zhang  
1st Author's Affiliation Waseda Univ. (Waseda U.)
2nd Author's Name Eiji Fukuzawa  
2nd Author's Affiliation Waseda Univ./Yazaki Corporation (Waseda U./Yazaki Corp.)
3rd Author's Name Jun Ohya  
3rd Author's Affiliation Waseda Univ. (Waseda U.)
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Speaker Author-1 
Date Time 2021-08-26 15:30:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2021-12 
Volume (vol) vol.121 
Number (no) no.155 
Page pp.30-35 
#Pages
Date of Issue 2021-08-19 (PRMU) 


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