Data-Driven Vehicle Identification by Image Matching

  • Jose A. Rodriguez-Serrano
  • Harsimrat Sandhawalia
  • Raja Bala
  • Florent Perronnin
  • Craig Saunders
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7584)


Vehicle identification from images has been predominantly addressed through automatic license plate recognition (ALPR) techniques which detect and recognize the characters in the plate region of the image. We move away from traditional ALPR techniques and advocate for a data-driven approach for vehicle identification. Here, given a plate image region, the idea is to search for a near-duplicate image in an annotated database; if found, the identity of the near-duplicate is transferred to the input region. Although this approach could be perceived as impractical, we actually demonstrate that it is feasible with state-of-the-art image representations, and that it presents some advantages in terms of speed, and time-to-deploy. To overcome the issue of identifying previously unseen identities, we propose an image simulation approach where photo-realistic images of license plates are generated for desired plate numbers. We demonstrate that there is no perceivable performance difference between using synthetic and real plates. We also improve the matching accuracy using similarity learning, which is in the spirit of domain adaptation.


Gaussian Mixture Model Image Retrieval Plate Image Image Match License Plate 
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

  • Jose A. Rodriguez-Serrano
    • 1
  • Harsimrat Sandhawalia
    • 1
  • Raja Bala
    • 2
  • Florent Perronnin
    • 1
  • Craig Saunders
    • 1
  1. 1.Xerox Research Centre EuropeMeylanFrance
  2. 2.Xerox Research Center WebsterUSA

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