Improving person re-identification via Pose-aware Multi-shot Matching

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Person re-identification is the problem of recognizing people across images or videos from non-overlapping views. Although there has been much progress in person re-identification for the last decade, it still remains a challenging task because of severe appearance changes of a person due to diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person reidentification by analyzing camera viewpoints and person poses, so-called Pose-aware Multi-shot Matching (PaMM), which robustly estimates target poses and efficiently conducts multi-shot matching based on the target pose information. Experimental results using public person reidentification datasets show that the proposed methods are promising for person re-identification under diverse viewpoints and pose variances.
Publisher
IEEE Computer Society and the Computer Vision Foundation (CVF)
Issue Date
2016-06-26
Language
English
Citation

2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp.1354 - 1362

DOI
10.1109/CVPR.2016.151
URI
http://hdl.handle.net/10203/244628
Appears in Collection
ME-Conference Papers(학술회의논문)
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