Pedestrian attribute recognition: A survey
•The first survey paper for pedestrian attributes recognition (PAR).•Give a brief introduction to existing pedestrian attributes recognition algorithms.•Give various research directions for PAR. Pedestrian Attribute Recognition (PAR) is an important task in computer vision community and plays an imp...
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| Published in | Pattern recognition Vol. 121; p. 108220 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0031-3203 1873-5142 |
| DOI | 10.1016/j.patcog.2021.108220 |
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| Abstract | •The first survey paper for pedestrian attributes recognition (PAR).•Give a brief introduction to existing pedestrian attributes recognition algorithms.•Give various research directions for PAR.
Pedestrian Attribute Recognition (PAR) is an important task in computer vision community and plays an important role in practical video surveillance. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attribute recognition, including the fundamental concepts and formulation of pedestrian attributes and corresponding challenges. Secondly, we analyze popular solutions for this task from eight perspectives. Thirdly, we discuss the specific attribute recognition, then, give a comparison between deep learning and traditional algorithm based PAR methods. After that, we show the connections between PAR and other computer vision tasks. Fourthly, we introduce the benchmark datasets, evaluation metrics in this community, and give a brief performance comparison. Finally, we summarize this paper and give several possible research directions for PAR. The project page of this paper can be found at: https://sites.google.com/view/ahu-pedestrianattributes/. |
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| AbstractList | •The first survey paper for pedestrian attributes recognition (PAR).•Give a brief introduction to existing pedestrian attributes recognition algorithms.•Give various research directions for PAR.
Pedestrian Attribute Recognition (PAR) is an important task in computer vision community and plays an important role in practical video surveillance. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attribute recognition, including the fundamental concepts and formulation of pedestrian attributes and corresponding challenges. Secondly, we analyze popular solutions for this task from eight perspectives. Thirdly, we discuss the specific attribute recognition, then, give a comparison between deep learning and traditional algorithm based PAR methods. After that, we show the connections between PAR and other computer vision tasks. Fourthly, we introduce the benchmark datasets, evaluation metrics in this community, and give a brief performance comparison. Finally, we summarize this paper and give several possible research directions for PAR. The project page of this paper can be found at: https://sites.google.com/view/ahu-pedestrianattributes/. |
| ArticleNumber | 108220 |
| Author | Zheng, Aihua Tang, Jin Zheng, Shaofei Yang, Rui Luo, Bin Chen, Zhe Wang, Xiao |
| Author_xml | – sequence: 1 givenname: Xiao surname: Wang fullname: Wang, Xiao organization: School of Computer Science and Technology, Anhui University, Hefei, China – sequence: 2 givenname: Shaofei surname: Zheng fullname: Zheng, Shaofei organization: School of Computer Science and Technology, Anhui University, Hefei, China – sequence: 3 givenname: Rui surname: Yang fullname: Yang, Rui organization: School of Computer Science and Technology, Anhui University, Hefei, China – sequence: 4 givenname: Aihua surname: Zheng fullname: Zheng, Aihua organization: School of Artificial Intelligence, Anhui University, Hefei, China – sequence: 5 givenname: Zhe surname: Chen fullname: Chen, Zhe organization: School of Computer Science, Faculty of Engineering, The University of Sydney, Australia – sequence: 6 givenname: Jin surname: Tang fullname: Tang, Jin organization: School of Computer Science and Technology, Anhui University, Hefei, China – sequence: 7 givenname: Bin surname: Luo fullname: Luo, Bin email: luobin@ahu.edu.cn organization: School of Computer Science and Technology, Anhui University, Hefei, China |
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Attribute-aware attention model for fine-grained representation learning |
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