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Paper Abstract and Keywords
Presentation 2018-12-14 16:20
Correcting Outputs of Ensemble LSTMs by CRF for Robust Activity Recognition
Haruka Abe, Yuta Hayakawa (Tokyo Tech), Takuya Hino, Motohide Sugihara, Hiroki Ikeya (KOMATSU), Masamichi Shimosaka (Tokyo Tech) PRMU2018-94
Abstract (in Japanese) (See Japanese page) 
(in English) Recognizing the activities of the construction vehicle helps to assess the skill of the workers or give a technical guidance to improve the workers' skill. Many research employ LSTM which can utilize long-term dependency for the activity recognition tasks. When we apply LSTM to the activity recognition tasks, we obtain multiple prediction results for data at the same frame. Conventional methods tend to employ an output only from the last frame of LSTM and others are disposed. Prediction accuracy can be improved by integrating these multiple outputs, but there is no current study. In this research, we tackle this issue by employing CRF. We'll show the proposed method overcomes the other methods throughout the experiment.
Keyword (in Japanese) (See Japanese page) 
(in English) Activity recognition / Construction vehicle / LSTM / CRF / Deep learning / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 362, PRMU2018-94, pp. 103-108, Dec. 2018.
Paper # PRMU2018-94 
Date of Issue 2018-12-06 (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 PRMU2018-94

Conference Information
Committee PRMU  
Conference Date 2018-12-13 - 2018-12-14 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2018-12-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Correcting Outputs of Ensemble LSTMs by CRF for Robust Activity Recognition 
Sub Title (in English)  
Keyword(1) Activity recognition  
Keyword(2) Construction vehicle  
Keyword(3) LSTM  
Keyword(4) CRF  
Keyword(5) Deep learning  
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Keyword(8)  
1st Author's Name Haruka Abe  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
2nd Author's Name Yuta Hayakawa  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
3rd Author's Name Takuya Hino  
3rd Author's Affiliation Komatsu Ltd. (KOMATSU)
4th Author's Name Motohide Sugihara  
4th Author's Affiliation Komatsu Ltd. (KOMATSU)
5th Author's Name Hiroki Ikeya  
5th Author's Affiliation Komatsu Ltd. (KOMATSU)
6th Author's Name Masamichi Shimosaka  
6th Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
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Speaker Author-1 
Date Time 2018-12-14 16:20:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2018-94 
Volume (vol) vol.118 
Number (no) no.362 
Page pp.103-108 
#Pages 6 
Date of Issue 2018-12-06 (PRMU) 

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