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| Presentation | 2022-12-16 15:55 Improving Pedestrian Attribute Recognition with Spatial Attention and Attribute Correlation Yichen Chen, Tetsuya Matsumoto (Nagoya Univ.), Yoshinori Takeuchi (Daido Univ.), Hiroaki Kudo (Nagoya Univ.)  IMQ2022-18 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In the field of video surveillance, pedestrian attribute recognition is a focus technical thema of study. To predict certain attributes of a pedestrian, it is necessary to localize areas associated with such attributes from an image since region annotations for this task are unavailable. Previous methods try that they introduce heuristic body-part localization processes to improve local feature representations. However, they didn't use attributes to define local feature regions. In addition, correlations between attributes have not been utilized. In real-world video surveillance scenarios, visual pedestrian attributes such as gender, clothing color, and so on, are crucial for pedestrian attribute recognition. According to the correlation of attributes, it is considered useful to localize the regions. In this report, we propose an attribute estimation network from pedestrians based on spatial attention maps and attribute correlations. Spatial attention can help the network focus on where each attribute should be concerned, and the correlation between attributes helps to reduce some incorrect predictions. In the experiments using pedestrian attribute datasets, we obtained the results that the proposed method estimates binary attributes with roughly 90% accuracy and categorical attributes with around 75% accuracy. For the mA and F1 metrics, the Attribute correlation attention module can enhance performance by 1.36% and 1.57%, respectively. The Spatial attention module can also improve performance by 0.57% and 0.67%. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | video surveillance / pedestrian attribute recognition / spatial attention / attributes correlation / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 317, IMQ2022-18, pp. 16-21, Dec. 2022. | |
| Paper # | IMQ2022-18 | |
| Date of Issue | 2022-12-09 (IMQ) | |
| 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 | IMQ2022-18 |
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| Conference Information | |
| Committee | IMQ |
| Conference Date | 2022-12-16 - 2022-12-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nishi-Chiba Campus, Chiba Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Image Media Quality, etc. |
| Paper Information | |
| Registration To | IMQ |
| Conference Code | 2022-12-IMQ |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Improving Pedestrian Attribute Recognition with Spatial Attention and Attribute Correlation |
| Sub Title (in English) | |
| Keyword(1) | video surveillance |
| Keyword(2) | pedestrian attribute recognition |
| Keyword(3) | spatial attention |
| Keyword(4) | attributes correlation |
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| 1st Author's Name | Yichen Chen |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | Tetsuya Matsumoto |
| 2nd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 3rd Author's Name | Yoshinori Takeuchi |
| 3rd Author's Affiliation | Daido University (Daido Univ.) |
| 4th Author's Name | Hiroaki Kudo |
| 4th Author's Affiliation | Nagoya University (Nagoya Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-12-16 15:55:00 |
| Presentation Time | 25 minutes |
| Registration for | IMQ |
| Paper # | IMQ2022-18 |
| Volume (vol) | vol.122 |
| Number (no) | no.317 |
| Page | pp.16-21 |
| #Pages | 6 |
| Date of Issue | 2022-12-09 (IMQ) |
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