PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification
This addresses the problem of recognizing individuals in surveillance or security applications, but it appears incremental as it builds on existing multi-shot matching with pose analysis.
The paper tackles the challenge of person re-identification across diverse camera viewpoints and poses by proposing a Pose-aware Multi-shot Matching (PaMM) framework, which outperforms state-of-the-art methods on public datasets.
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.