Improving Person Re-Identification via Pose-Aware Multi-Shot Matching

Yeong-Jun Cho, Kuk-Jin Yoon; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1354-1362

Abstract


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 re-identification 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 re-identification dataset show that the proposed methods are promising for person re-identification under diverse viewpoints and pose variances.

Related Material


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[bibtex]
@InProceedings{Cho_2016_CVPR,
author = {Cho, Yeong-Jun and Yoon, Kuk-Jin},
title = {Improving Person Re-Identification via Pose-Aware Multi-Shot Matching},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}