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

arXiv: https://arxiv.org/abs/1705.06011 · HTML: https://mlsanity.com/paper/1705.06011/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2017.

## Built on by (most cited first, 5 of 5)

- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [Exploring Modality-shared Appearance Features and Modality-invariant Relation Features for Cross-modality Person Re-Identification](https://mlsanity.com/paper/2104.11539/) (2021)
- [A Brief Survey on Person Recognition at a Distance](https://mlsanity.com/paper/2212.08969/) (2022)
- [Large-scale Training Data Search for Object Re-identification](https://mlsanity.com/paper/2303.16186/) (2023)
- [Unsupervised Domain Adaptive Person Re-id with Local-enhance and Prototype Dictionary Learning](https://mlsanity.com/paper/2201.03803/) (2022)

## Abstract (arXiv)

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.