Abstract:
This paper proposes a fast algorithm detecting
license plates in various conditions. There are three main
contributions in this paper. The first contribution is that we
define a new vertical edge map, with which the license plate
detection algorithm is extremely fast. The second contribution is
that we construct a cascade classifier which is composed of two
kinds of classifiers. The classifiers based on statistical features
decrease the complexity of the system. They are followed by the
classifiers based on Haar-features, which make it possible to
detect license plate in various conditions. Our algorithm is
robust to the variance of the illumination, view angle, the
position, size and color of the license plates when working in
complex environment. The third contribution is that we
experimentally analyze the relations of the scaling factor with
detection rate and processing time. On the basis of the analysis,
we select the optimal scaling factor in our algorithm. In the
experiments, both high detection rate (with low false positive
rate) and high speed are achieved when the algorithm is used to
detect license plates in various complex conditions.
