Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios
文献类型：会议论文
| 作者 | Dangwei Li ; Xiaotang Chen; Kaiqi Huang
|
| 出版日期 | 2015 |
| 会议日期 | 2015-11-01 |
| 会议地点 | Kuala Lumpur, Malaysia |
| 关键词 | Multi-attribute Learning |
| 页码 | 1-5 |
| 英文摘要 | 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. |
| 会议录 | Proc. Asian Conference on Pattern Recognition 2015
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12681]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Kaiqi Huang |
| 作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Dangwei Li,Xiaotang Chen,Kaiqi Huang. Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios[C]. 见:. Kuala Lumpur, Malaysia. 2015-11-01. |
入库方式： OAI收割
来源：自动化研究所
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