| Multi-label convolutional neural network based pedestrian attribute classification | |
Zhu, Jianqing1; Liao, Shengcai2 ; Lei, Zhen2; Li, Stan Z.2
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| Source Publication | IMAGE AND VISION COMPUTING
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| 2017-02-01 | |
| Volume | 58Pages:224-229 |
| Subtype | Article |
| Abstract | Recently, pedestrian attributes like gender, age, clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network(MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Experiments show that the proposed method significantly outperforms the SVM based method on the PETA database. (C) 2016 Elsevier B.V. All rights reserved. |
| Keyword | Pedestrian Attribute Classification Multi-label Classification Convolutional Neural Network |
| WOS Headings | Science & Technology ; Technology ; Physical Sciences |
| DOI | 10.1016/j.imavis.2016.07.004 |
| WOS Keyword | SOFT BIOMETRICS |
| Indexed By | SCI |
| Language | 英语 |
| Funding Organization | Scientific Research Funds of Huaqiao University(16BS108) ; National Natural Science Foundation of China(61602191 ; Chinese Academy of Sciences Project(KGZD-EW-102-2) ; 61375037 ; 61473291 ; 61572501 ; 61502491 ; 61572536) |
| WOS Research Area | Computer Science ; Engineering ; Optics |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic ; Optics |
| WOS ID | WOS:000395844700021 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/14433 |
| Collection | 模式识别国家重点实验室_生物识别与安全技术研究 |
| Affiliation | 1.Huaqiao Univ, Coll Engn, Quanzhou 362021, Fujian, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Ctr Biometr & Secur Res, Beijing 100190, Peoples R China |
| Recommended Citation GB/T 7714 | Zhu, Jianqing,Liao, Shengcai,Lei, Zhen,et al. Multi-label convolutional neural network based pedestrian attribute classification[J]. IMAGE AND VISION COMPUTING,2017,58:224-229. |
| APA | Zhu, Jianqing,Liao, Shengcai,Lei, Zhen,&Li, Stan Z..(2017).Multi-label convolutional neural network based pedestrian attribute classification.IMAGE AND VISION COMPUTING,58,224-229. |
| MLA | Zhu, Jianqing,et al."Multi-label convolutional neural network based pedestrian attribute classification".IMAGE AND VISION COMPUTING 58(2017):224-229. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| JQZHU-IVC-2017.pdf（1036KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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