Improving Person Re-identification via Pose-Aware Multi-shot Matching

Yeong-Jun Cho, Kuk-Jin Yoon
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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
more » ... 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.
doi:10.1109/cvpr.2016.151 dblp:conf/cvpr/ChoY16 fatcat:spapzfambrfgliwn5mosvxx5ie