PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification
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arXiv
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| Format: | Preprint |
| Published: |
2017
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| _version_ | 1866909233223565312 |
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| author | Cho, Yeong-Jun Yoon, Kuk-Jin |
| author_facet | Cho, Yeong-Jun Yoon, Kuk-Jin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_1705_06011 |
| institution | arXiv |
| publishDate | 2017 |
| record_format | arxiv |
| spellingShingle | PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification Cho, Yeong-Jun Yoon, Kuk-Jin Computer Vision and Pattern Recognition 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. |
| title | PaMM: Pose-aware Multi-shot Matching for Improving Person Re-identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1705.06011 |