Relaxed Pairwise Learned Metric for Person Re-identification

Martin Hirzer, Peter Roth, Martin Köstinger, Horst Bischof

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the European Conference on Computer Vision (ECCV)
Publication statusPublished - 2012
Event12th European Conference on Computer Vision: ECCV 2012 - Florenz, Italy
Duration: 7 Oct 201213 Oct 2012


Conference12th European Conference on Computer Vision
Abbreviated titleECCV 2012

Fields of Expertise

  • Information, Communication & Computing

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