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- Publisher Website: 10.1145/2647868.2654966
- Scopus: eid_2-s2.0-84913526249
- WOS: WOS:000482104200123
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Conference Paper: Pedestrian attribute recognition at far distance
Title | Pedestrian attribute recognition at far distance |
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Authors | |
Keywords | Large-scale database Attribute classification |
Issue Date | 2014 |
Citation | MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia, 2014, p. 789-792 How to Cite? |
Abstract | The capability of recognizing pedestrian attributes, such as gender and clothing style, at far distance, is of practical interest in far-view surveillance scenarios where face and body close-shots are hardly available. We make two contributions in this paper. First, we release a new pedestrian attribute dataset, which is by far the largest and most diverse of its kind. We show that the large-scale dataset facilitates the learning of robust attribute detectors with good generalization performance. Second, we present the benchmark performance by SVM-based method and propose an alternative approach that exploits context of neighboring pedestrian images for improved attribute inference. |
Persistent Identifier | http://hdl.handle.net/10722/273705 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Deng, Yubin | - |
dc.contributor.author | Luo, Ping | - |
dc.contributor.author | Loy, Chen Change | - |
dc.contributor.author | Tang, Xiaoou | - |
dc.date.accessioned | 2019-08-12T09:56:25Z | - |
dc.date.available | 2019-08-12T09:56:25Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia, 2014, p. 789-792 | - |
dc.identifier.uri | http://hdl.handle.net/10722/273705 | - |
dc.description.abstract | The capability of recognizing pedestrian attributes, such as gender and clothing style, at far distance, is of practical interest in far-view surveillance scenarios where face and body close-shots are hardly available. We make two contributions in this paper. First, we release a new pedestrian attribute dataset, which is by far the largest and most diverse of its kind. We show that the large-scale dataset facilitates the learning of robust attribute detectors with good generalization performance. Second, we present the benchmark performance by SVM-based method and propose an alternative approach that exploits context of neighboring pedestrian images for improved attribute inference. | - |
dc.language | eng | - |
dc.relation.ispartof | MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia | - |
dc.subject | Large-scale database | - |
dc.subject | Attribute classification | - |
dc.title | Pedestrian attribute recognition at far distance | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1145/2647868.2654966 | - |
dc.identifier.scopus | eid_2-s2.0-84913526249 | - |
dc.identifier.spage | 789 | - |
dc.identifier.epage | 792 | - |
dc.identifier.isi | WOS:000482104200123 | - |