Learning Implicit Transfer for Person Re-identification

  • Tamar Avraham
  • Ilya Gurvich
  • Michael Lindenbaum
  • Shaul Markovitch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)


This paper proposes a novel approach for pedestrian re-identification. Previous re-identification methods use one of 3 approaches: invariant features; designing metrics that aim to bring instances of shared identities close to one another and instances of different identities far from one another; or learning a transformation from the appearance in one domain to the other. Our implicit approach models camera transfer by a binary relation R = {(x,y)|x and y describe the same person seen from cameras A and B respectively}. This solution implies that the camera transfer function is a multi-valued mapping and not a single-valued transformation, and does not assume the existence of a metric with desirable properties. We present an algorithm that follows this approach and achieves new state-of-the-art performance.


Binary Relation True Match Positive Pair Negative Pair Cumulative Match Characteristic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tamar Avraham
    • 1
  • Ilya Gurvich
    • 1
  • Michael Lindenbaum
    • 1
  • Shaul Markovitch
    • 1
  1. 1.Computer Science DepartmentTechnion - I.I.T.HaifaIsrael

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