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Paper Abstract and Keywords
Presentation 2018-12-14 11:00
[Short Paper] Skeleton-based Human Action Recognition with Fine-to-Coarse Convolutional Neural Network
Thao Minh Le, Nakamasa Inoue, Koichi Shinoda (TokyoTech) PRMU2018-86
Abstract (in Japanese) (See Japanese page) 
(in English) This work introduces a new framework for skeleton-based human action recognition. Existing approaches using Convolutional Neural Network (CNN) often suffer from the insufficiency problem of training data. In this study, we utilize a fine-to-coarse (F2C) CNN architecture that is come up based on the special structure of human skeletal data. We evaluate our proposed method on two skeletal datasets publicly available, namely NTU RGB+D and SBU Kinect Interaction dataset. It achieves 79.6% and 84.6% of accuracies on NTU RGB+D with cross-object and cross-view protocol, respectively, which are almost identical with the state-of-the-art performance. In addition, our method significantly improves the accuracy of the actions in two-person interactions.
Keyword (in Japanese) (See Japanese page) 
(in English) Action Recognition / Deep Learning / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 362, PRMU2018-86, pp. 61-64, Dec. 2018.
Paper # PRMU2018-86 
Date of Issue 2018-12-06 (PRMU) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 2018-12-13 - 2018-12-14 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To PRMU 
Conference Code 2018-12-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Skeleton-based Human Action Recognition with Fine-to-Coarse Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) Action Recognition  
Keyword(2) Deep Learning  
1st Author's Name Thao Minh Le  
1st Author's Affiliation Tokyo Institute of Technology (TokyoTech)
2nd Author's Name Nakamasa Inoue  
2nd Author's Affiliation Tokyo Institute of Technology (TokyoTech)
3rd Author's Name Koichi Shinoda  
3rd Author's Affiliation Tokyo Institute of Technology (TokyoTech)
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Date Time 2018-12-14 11:00:00 
Presentation Time 15 
Registration for PRMU 
Paper # PRMU2018-86 
Volume (vol) 118 
Number (no) no.362 
Page pp.61-64 
Date of Issue 2018-12-06 (PRMU) 

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