Person Re-Identification by Attribute-Assisted Clothes Appearance

Person re-identification across nonoverlapping camera views is a challenging computer vision task. Due to the often low video quality and high camera position, it is difficult to get clear human faces. Therefore, clothes appearance is the main cue to re-identify a person. It is difficult to represen...

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Bibliographic Details
Published inPerson Re-Identification pp. 119 - 138
Main Authors Li, Annan, Liu, Luoqi, Yan, Shuicheng
Format Reference Book Chapter
LanguageEnglish
Published United Kingdom Springer London, Limited 2014
Springer London
SeriesAdvances in Computer Vision and Pattern Recognition
Subjects
Clothing Appearance
Clothing Properties
Daily Clothing
Image processing
Latent SVM
Maths for computer scientists
Non-overlapping Camera Views
Pattern recognition
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Summary:Person re-identification across nonoverlapping camera views is a challenging computer vision task. Due to the often low video quality and high camera position, it is difficult to get clear human faces. Therefore, clothes appearance is the main cue to re-identify a person. It is difficult to represent clothes appearance using low-level features due to its nonrigidity, but daily clothes have many characteristics in common. Based on this observation, we study person re-identification by embedding middle-level clothes attributes into the classifier via a latent support vector machine framework. We also collect a large-scale person re-identification dataset, and the effectiveness of the proposed method is demonstrated on this dataset under open-set experimental settings.
Bibliography:These authors Annan Li and Luoqi Liu contributed equally to this work.
ISBN:1447162951
9781447162957
ISSN:2191-6586
2191-6594
DOI:10.1007/978-1-4471-6296-4_6