# PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification

## TL;DR

This paper introduces PaMM, a pose-aware multi-shot matching framework that improves person re-identification accuracy by analyzing camera viewpoints and poses, outperforming existing methods across diverse scenarios.

## Contribution

The paper presents a novel pose-aware framework that robustly estimates poses and enhances multi-shot matching for person re-identification, addressing pose and viewpoint variations.

## Key findings

- Outperforms state-of-the-art re-identification methods
- Effective in diverse viewpoints and pose variations
- Demonstrates robustness on public datasets

## Abstract

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.