José A. Rodriguez, Harsimrat Sandhawalia, Raja Bala, Florent Perronnin, Craig Saunders
12th European Conference on Computer Vision, Firenze, Italy, 7-13 October, 2012.
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-ofthe-
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.
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