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Paper Abstract and Keywords
Presentation 2020-10-09 15:15
Trajectory Forecasting using Deep Learning: A Survey
Horoaki Minoura, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi (Chubu Univ.) PRMU2020-29
Abstract (in Japanese) (See Japanese page) 
(in English) Trajectory forecasting is the technology of predicting the path along which moving objects such as a pedestrian and vehicle will move in the future. It has been studied for a long time using Bayesian model and Social-Force model. On the other hand, it has changed dramatically to the method using Convolutional Neural Network and Long Short-Term Memory by the development of Deep Learning (DL). These methods predict high-precision paths by modeling and combining factors such as different viewpoints, person-location, and object information. In this paper, we survey trajectory forecasting methods with DL and systematically summarize various prediction methods. We also introduce the datasets and the evaluation metrics used for the quantitative evaluation.Furthermore, we evaluate prediction accuracy with multiple datasets about the typical methods and discuss the accuracy and the prediction result of each model.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Trajectory Forecasting / Dataset / Evaluation metrics / Survey / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 187, PRMU2020-29, pp. 62-78, Oct. 2020.
Paper # PRMU2020-29 
Date of Issue 2020-10-02 (PRMU) 
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 PRMU2020-29

Conference Information
Committee PRMU  
Conference Date 2020-10-09 - 2020-10-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Recognition and understating of human 
Paper Information
Registration To PRMU 
Conference Code 2020-10-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Trajectory Forecasting using Deep Learning: A Survey 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Trajectory Forecasting  
Keyword(3) Dataset  
Keyword(4) Evaluation metrics  
Keyword(5) Survey  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Horoaki Minoura  
1st Author's Affiliation Chubu University (Chubu Univ.)
2nd Author's Name Tsubasa Hirakawa  
2nd Author's Affiliation Chubu University (Chubu Univ.)
3rd Author's Name Takayoshi Yamashita  
3rd Author's Affiliation Chubu University (Chubu Univ.)
4th Author's Name Hironobu Fujiyoshi  
4th Author's Affiliation Chubu University (Chubu Univ.)
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Speaker Author-1 
Date Time 2020-10-09 15:15:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2020-29 
Volume (vol) vol.120 
Number (no) no.187 
Page pp.62-78 
#Pages 17 
Date of Issue 2020-10-02 (PRMU) 

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