| Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios | |
Dangwei Li ; Xiaotang Chen; Kaiqi Huang
| |
| 2015 | |
| Conference Name | Asian Conference on Pattern Recognition |
| Source Publication | Proc. Asian Conference on Pattern Recognition 2015 |
| Pages | 1-5 |
| Conference Date | 2015-11-01 |
| Conference Place | Kuala Lumpur, Malaysia |
| Abstract | In real video surveillance scenarios, visual pedestrian attributes, such as gender, backpack, clothes types, are very important for pedestrian retrieval and person re-identification. Existing methods for attributes recognition have two drawbacks: (a) handcrafted features (e.g. color histograms, local binary patterns) cannot cope well with the difficulty of real video surveillance scenarios; (b) the relationship among pedestrian attributes is ignored. To address the two drawbacks, we propose two deep learning based models to recognize pedestrian attributes. On the one hand, each attribute is treated as an independent component and the deep learning based single attribute recognition model (DeepSAR) is proposed to recognize each attribute one by one. On the other hand, to exploit the relationship among attributes, the deep learning framework which recognizes multiple attributes jointly (DeepMAR) is proposed. In the DeepMAR, one attribute can contribute to the representation of other attributes. For example, the gender of woman can contribute to the representation of long hair and wearing skirt. Experiments on recent popular pedestrian attribute datasets illustrate that our proposed models achieve the state-of-the-art results. |
| Keyword | Multi-attribute Learning |
| Indexed By | EI |
| Language | 英语 |
| Document Type | 会议论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/12681 |
| Collection | 智能感知与计算研究中心 |
| Corresponding Author | Kaiqi Huang |
| Affiliation | 中国科学院自动化研究所 |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Dangwei Li,Xiaotang Chen,Kaiqi Huang. Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios[C],2015:1-5. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| multiattribute_acpr1（247KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Download | ||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment