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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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

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Published: 01 May 2015

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Fang MLiu QZhou YDu XLiang QLiu SLarson K(2024)A teacher classroom dress assessment method based on a new assessment datasetProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/802(7251-7259)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/802
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
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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
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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
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