Abstract

Person re-identifification is the problem of recognizing people across images or videos from non-overlapping views. Although there has been much progress in person re-identifification 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 reidentifification by analyzing camera viewpoints and person poses, so-called Pose-aware Multi-shot Matching (PaMM), which robustly estimates target poses and effificiently conducts multi-shot matching based on the target pose information. Experimental results using public person reidentifification datasets show that the proposed methods are promising for person re-identifification under diverse viewpoints and pose variances.