research-article

Clothing Attributes Assisted Person Reidentification

Authors:

Shuicheng YanAuthors Info & Claims

IEEE Transactions on Circuits and Systems for Video Technology, Volume 25, Issue 5

Pages 869 - 878

https://doi.org/10.1109/TCSVT.2014.2352552

Published: 01 May 2015 Publication History

Abstract

Person reidentification across nonoverlapping camera views is a rather challenging task. Due to the difficulties in obtaining identifiable faces, clothing appearance becomes the main cue for identification purposes. In this paper, we present a comprehensive study on clothing attributes assisted person reidentification. First, the body parts and their local features are extracted for alleviating the pose-misalignment issue. A latent support vector machine (LSVM)-based person reidentification approach is proposed to describe the relations among the low-level part features, middle-level clothing attributes, and high-level reidentification labels of person pairs. Motivated by the uncertainties of clothing attributes, we treat them as real-value variables instead of using them as discrete variables. Moreover, a large-scale real-world dataset with 10 camera views and about 200 subjects is collected and thoroughly annotated for this paper. The extensive experiments on this dataset show: 1) part features are more effective than features extracted from the holistic human bounding boxes; 2) the clothing attributes embedded in the LSVM model may further boost reidentification performance compared with support vector machine without clothing attributes; and 3) treating clothing attributes as real-value variables is more effective than using them as discrete variables in person reidentification.

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Cited By

Gong XLuo B(2024)Video-based person re-identification with scene and person attributesMultimedia Tools and Applications10.1007/s11042-023-15719-w83:3(8117-8128)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15719-w
Lv JXiong ZZou RWen ZLin H(2022)Feature Fusion with Non-local for Pedestrian Attribute RecognitionProceedings of the 2022 2nd International Conference on Bioinformatics and Intelligent Computing10.1145/3523286.3524581(421-428)Online publication date: 21-Jan-2022
https://dl.acm.org/doi/10.1145/3523286.3524581
Chai TChen ZLi AChen JMei XWang Y(2022)Video Person Re-Identification Using Attribute-Enhanced FeaturesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.318902732:11(7951-7966)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1109/TCSVT.2022.3189027
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Index Terms

Clothing Attributes Assisted Person Reidentification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Learning paradigms

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Circuits and Systems for Video Technology

IEEE Transactions on Circuits and Systems for Video Technology Volume 25, Issue 5

May 2015

180 pages

ISSN:1051-8215

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Copyright © 2014.

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IEEE Press

Publication History

Published: 01 May 2015

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Cited By

Gong XLuo B(2024)Video-based person re-identification with scene and person attributesMultimedia Tools and Applications10.1007/s11042-023-15719-w83:3(8117-8128)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15719-w
Lv JXiong ZZou RWen ZLin H(2022)Feature Fusion with Non-local for Pedestrian Attribute RecognitionProceedings of the 2022 2nd International Conference on Bioinformatics and Intelligent Computing10.1145/3523286.3524581(421-428)Online publication date: 21-Jan-2022
https://dl.acm.org/doi/10.1145/3523286.3524581
Chai TChen ZLi AChen JMei XWang Y(2022)Video Person Re-Identification Using Attribute-Enhanced FeaturesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.318902732:11(7951-7966)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1109/TCSVT.2022.3189027
Kaur NPandey S(2022)Predicting clothing attributes with CNN and SURF based classification modelMultimedia Tools and Applications10.1007/s11042-022-13714-182:7(10681-10701)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1007/s11042-022-13714-1
Khan MAbu-Khalaf JSuter DRosenhahn B(2022)M3T: Multi-class Multi-instance Multi-view Object Tracking for Embodied AI TasksImage and Vision Computing10.1007/978-3-031-25825-1_18(246-261)Online publication date: 23-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-25825-1_18
Wu DLi ZZhou JGan JGao WLi H(2022)Clothing attribute recognition via a holistic relation networkInternational Journal of Intelligent Systems10.1002/int.2284037:9(6201-6220)Online publication date: 30-Jul-2022
https://dl.acm.org/doi/10.1002/int.22840
Song WZheng JWu YChen CLiu F(2021)Discriminative feature extraction for video person re-identification via multi-task networkApplied Intelligence10.1007/s10489-020-01844-851:2(788-803)Online publication date: 1-Feb-2021
https://dl.acm.org/doi/10.1007/s10489-020-01844-8
Chen XCao J(2020)Part-Based Pedestrian Attribute AnalysisProceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems10.1145/3440840.3440846(124-130)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3440840.3440846
Yin JFan ZChen SWang Y(2020)In-depth exploration of attribute information for person re-identificationApplied Intelligence10.1007/s10489-020-01752-x50:11(3607-3622)Online publication date: 1-Nov-2020
https://dl.acm.org/doi/10.1007/s10489-020-01752-x
Zheng QHe ZLiang CChen JLin CTao D(2020)Transferring fashion to surveillance with weak labelsNeural Computing and Applications10.1007/s00521-020-05528-935:18(13021-13035)Online publication date: 23-Nov-2020
https://dl.acm.org/doi/10.1007/s00521-020-05528-9
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