Relaxed Pairwise Learned Metric for Person Re-identification

  • Martin Hirzer
  • Peter M. Roth
  • Martin Köstinger
  • Horst Bischof
Conference paper

DOI: 10.1007/978-3-642-33783-3_56

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)
Cite this paper as:
Hirzer M., Roth P.M., Köstinger M., Bischof H. (2012) Relaxed Pairwise Learned Metric for Person Re-identification. In: Fitzgibbon A., Lazebnik S., Perona P., Sato Y., Schmid C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7577. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Martin Hirzer
    • 1
  • Peter M. Roth
    • 1
  • Martin Köstinger
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyAustria

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