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Abstract
Matching persons across non-overlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples from different cameras. However, most of these approaches ignore the transition from one camera to the other. In this paper, we propose to learn a metric from pairs of samples from different cameras. In this way, even less sophisticated features describing color and texture information are sufficient for finally getting state-of-the-art classification results. Moreover, once the metric has been learned, only linear projections are necessary at search time, where a simple nearest neighbor classification is performed. The approach is demonstrated on three publicly available datasets of different complexity, where it can be seen that state-of-the-art results can be obtained at much lower computational costs.
Originalsprache | englisch |
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Titel | Proceedings of the European Conference on Computer Vision (ECCV) |
Herausgeber (Verlag) | . |
Seiten | 780-793 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2012 |
Veranstaltung | 12th European Conference on Computer Vision: ECCV 2012 - Florenz, Italien Dauer: 7 Okt. 2012 → 13 Okt. 2012 |
Konferenz
Konferenz | 12th European Conference on Computer Vision |
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Kurztitel | ECCV 2012 |
Land/Gebiet | Italien |
Ort | Florenz |
Zeitraum | 7/10/12 → 13/10/12 |
Fields of Expertise
- Information, Communication & Computing
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Poster Presentation: Relaxed Pairwise Learned Metric for Person Re-Identification
Martin Hirzer (Redner/in)
7 Okt. 2012 → 13 Okt. 2012Aktivität: Vortrag oder Präsentation › Posterpräsentation › Science to science