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| Paper Abstract and Keywords | ||
| Presentation | 2020-03-16 14:40 Egocentric pedestrian motion prediction by separately modeling body pose and position Donghao Wu, Takuma Yagi, Yusuke Matsui, Yoichi Sato (the Univ. of Tokyo)  PRMU2019-72 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We study the problem of forecasting human's motion captured from egocentric videos. We propose a novel learning approach by separately modeling human pose and its corresponding scale and position with two deep learning modules, whose outputs are later combined to make the final prediction. Our proposed method successfully forecasts the position and body pose of the target person with an ideal scale, relieving from the mean convergence problem. The experiment is evaluated based on First-Person Locomotion (FPL) dataset. The predictions show the separate modeling approach has plausible-looking visualization results upon egocentric settings, outperforming the state-of-the-art methods which only consider modeling single pose granularity of human motion that suffers from the mean convergence results. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | motion forecasting / egocentric vision / human dynamics / deep learning / neural network / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 481, PRMU2019-72, pp. 39-44, March 2020. | |
| Paper # | PRMU2019-72 | |
| Date of Issue | 2020-03-09 (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 | PRMU2019-72 |
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| Conference Information | |
| Committee | PRMU IPSJ-CVIM |
| Conference Date | 2020-03-16 - 2020-03-17 |
| 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 | 2020-03-PRMU-CVIM |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Egocentric pedestrian motion prediction by separately modeling body pose and position |
| Sub Title (in English) | |
| Keyword(1) | motion forecasting |
| Keyword(2) | egocentric vision |
| Keyword(3) | human dynamics |
| Keyword(4) | deep learning |
| Keyword(5) | neural network |
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| Keyword(8) | |
| 1st Author's Name | Donghao Wu |
| 1st Author's Affiliation | The University of Tokyo (the Univ. of Tokyo) |
| 2nd Author's Name | Takuma Yagi |
| 2nd Author's Affiliation | The University of Tokyo (the Univ. of Tokyo) |
| 3rd Author's Name | Yusuke Matsui |
| 3rd Author's Affiliation | The University of Tokyo (the Univ. of Tokyo) |
| 4th Author's Name | Yoichi Sato |
| 4th Author's Affiliation | The University of Tokyo (the Univ. of Tokyo) |
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| Speaker | Author-1 |
| Date Time | 2020-03-16 14:40:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2019-72 |
| Volume (vol) | vol.119 |
| Number (no) | no.481 |
| Page | pp.39-44 |
| #Pages | 6 |
| Date of Issue | 2020-03-09 (PRMU) |
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