Improving Person Re identification via Pose Aware Multi shot Matching
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.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)