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Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios
Dangwei Li; Xiaotang Chen; Kaiqi Huang
2015
Conference NameAsian Conference on Pattern Recognition
Source PublicationProc. Asian Conference on Pattern Recognition 2015
Pages1-5
Conference Date2015-11-01
Conference PlaceKuala Lumpur, Malaysia
AbstractIn 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.
KeywordMulti-attribute Learning
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12681
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute 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.
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