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Learning Implicit Transfer for Person Re-identification

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Part of the Lecture Notes in Computer Science book series (LNIP,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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Avraham, T., Gurvich, I., Lindenbaum, M., Markovitch, S. (2012). Learning Implicit Transfer for Person Re-identification. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7583. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33862-5

  • Online ISBN: 978-3-642-33863-2

  • eBook Packages: Computer ScienceComputer Science (R0)