  Person re-identification is the problem of recognizing people across
different images or videos with non-overlapping views. Although there has been
much progress in person re-identification over the last decade, it remains a
challenging task because appearances of people can seem extremely different
across 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 in a so-called Pose-aware Multi-shot Matching (PaMM), which
robustly estimates people's poses and efficiently conducts multi-shot matching
based on pose information. Experimental results using public person
re-identification datasets show that the proposed methods outperform
state-of-the-art methods and are promising for person re-identification from
diverse viewpoints and pose variances.
