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| Presentation | 2017-02-18 14:35 Part-aware CNN for Pedestrian Detection Cong Cao, Yu Wang, Jien Kato, Kenji Mase (Nagoya Univ.)  PRMU2016-167 CNR2016-34 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Pedestrian detection is a significant task in computer vision. In recent years, it is widely used in the applications such as monitoring system and automatic drive. Although it has been exhaustively studied over the past decade, the occlusion situation remains a very challenging problem. In order to deal with this problem, one convincing method is to utilize the parts based methods for the visible parts information, and furthermore to estimate the pedestrian position. Many part-based pedestrian detection methods have been proposed in recent years. According to our analyses, clumsy part combining process have always been the problems to limit pedestrian detection performance. In this paper, we propose Part-aware CNN to solve this problem. In this study, we focus on the part detector combination phase, which including a brand new method to reform the part detectors to the convolutional layer of the CNN and optimize the whole pipeline by fine-tuning the CNN. In experiments, it shows the astonishing effectiveness of optimization and robustness of occlusion handling. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Pedestrian detection / Body parts detection / CNN / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 116, no. 461, PRMU2016-167, pp. 87-90, Feb. 2017. | |
| Paper # | PRMU2016-167 | |
| Date of Issue | 2017-02-11 (PRMU, CNR) | |
| ISSN | Print edition: ISSN 0913-5685 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 | PRMU2016-167 CNR2016-34 |
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| Conference Information | |
| Committee | PRMU CNR |
| Conference Date | 2017-02-18 - 2017-02-19 |
| 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 | 2017-02-PRMU-CNR |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Part-aware CNN for Pedestrian Detection |
| Sub Title (in English) | |
| Keyword(1) | Pedestrian detection |
| Keyword(2) | Body parts detection |
| Keyword(3) | CNN |
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| 1st Author's Name | Cong Cao |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | Yu Wang |
| 2nd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 3rd Author's Name | Jien Kato |
| 3rd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 4th Author's Name | Kenji Mase |
| 4th Author's Affiliation | Nagoya University (Nagoya Univ.) |
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| Speaker | Author-1 |
| Date Time | 2017-02-18 14:35:00 |
| Presentation Time | 25 minutes |
| Registration for | PRMU |
| Paper # | PRMU2016-167, CNR2016-34 |
| Volume (vol) | vol.116 |
| Number (no) | no.461(PRMU), no.462(CNR) |
| Page | pp.87-90 |
| #Pages | 4 |
| Date of Issue | 2017-02-11 (PRMU, CNR) |
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