Details
Translated title of the contribution | Personenwiedererkennung in einem Fischaugenkameranetzwerk mit unterschiedlichen Blickrichtungen |
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Original language | English |
Pages (from-to) | 263-274 |
Number of pages | 12 |
Journal | PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science |
Volume | 87 |
Issue number | 5-6 |
Publication status | Published - 28 Oct 2019 |
Abstract
Person re-identification is a crucial component for multi-camera networks in different real-world applications such as surveillance, automation, and business analytics. Despite considerable recent progress, the performance in practice is still not satisfactory due to the high intra-person variation and significant complexity of the task, including differences in scale, viewing direction, and illumination. We propose a novel approach for person re-identification, which exploits multi-view information of a fisheye camera looking downwards from the ceiling. To handle this highly variable multi-view information, we build a generic pipeline for processing fisheye camera imagery based on geometric sensor modelling and deep learning. The proposed approach is evaluated on a re-mapped version of the publicly available Market-1501 dataset, and, in addition, on a new fisheye dataset. Significant improvements are shown in our experiments: our approach achieves more than 97% rank#1 recognition rate if applied on the re-mapped Market-1501 dataset; on the new fisheye dataset we find an improvement of about 12% compared to random-view fusion.
Keywords
- Deep learning, Fisheye, Multi-view, Person re-identification
ASJC Scopus subject areas
- Social Sciences(all)
- Geography, Planning and Development
- Physics and Astronomy(all)
- Instrumentation
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Vol. 87, No. 5-6, 28.10.2019, p. 263-274.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Multi-view Person Re-identification in a Fisheye Camera Network with Different Viewing Directions
AU - Blott, Gregor
AU - Yu, Jie
AU - Heipke, Christian
PY - 2019/10/28
Y1 - 2019/10/28
N2 - Person re-identification is a crucial component for multi-camera networks in different real-world applications such as surveillance, automation, and business analytics. Despite considerable recent progress, the performance in practice is still not satisfactory due to the high intra-person variation and significant complexity of the task, including differences in scale, viewing direction, and illumination. We propose a novel approach for person re-identification, which exploits multi-view information of a fisheye camera looking downwards from the ceiling. To handle this highly variable multi-view information, we build a generic pipeline for processing fisheye camera imagery based on geometric sensor modelling and deep learning. The proposed approach is evaluated on a re-mapped version of the publicly available Market-1501 dataset, and, in addition, on a new fisheye dataset. Significant improvements are shown in our experiments: our approach achieves more than 97% rank#1 recognition rate if applied on the re-mapped Market-1501 dataset; on the new fisheye dataset we find an improvement of about 12% compared to random-view fusion.
AB - Person re-identification is a crucial component for multi-camera networks in different real-world applications such as surveillance, automation, and business analytics. Despite considerable recent progress, the performance in practice is still not satisfactory due to the high intra-person variation and significant complexity of the task, including differences in scale, viewing direction, and illumination. We propose a novel approach for person re-identification, which exploits multi-view information of a fisheye camera looking downwards from the ceiling. To handle this highly variable multi-view information, we build a generic pipeline for processing fisheye camera imagery based on geometric sensor modelling and deep learning. The proposed approach is evaluated on a re-mapped version of the publicly available Market-1501 dataset, and, in addition, on a new fisheye dataset. Significant improvements are shown in our experiments: our approach achieves more than 97% rank#1 recognition rate if applied on the re-mapped Market-1501 dataset; on the new fisheye dataset we find an improvement of about 12% compared to random-view fusion.
KW - Deep learning
KW - Fisheye
KW - Multi-view
KW - Person re-identification
UR - http://www.scopus.com/inward/record.url?scp=85074700731&partnerID=8YFLogxK
U2 - 10.1007/s41064-019-00083-y
DO - 10.1007/s41064-019-00083-y
M3 - Article
AN - SCOPUS:85074700731
VL - 87
SP - 263
EP - 274
JO - PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science
JF - PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science
SN - 2512-2789
IS - 5-6
ER -