Road sign classification using Laplace kernel classifier

Driver support systems (DSS) of intelligent vehicles will predict potentially dangerous situations in heavy traffic, help with navigation and vehicle guidance and interact with a human driver. Important information necessary for traffic situation understanding is presented by road signs. A new kerne...

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Bibliographic Details
Published in:Pattern recognition letters 2000-12, Vol.21 (13), p.1165-1173
Main Authors: Paclı́k, P., Novovičová, J., Pudil, P., Somol, P.
Format: Article
Language:English
Subjects:
Expectation–maximization algorithm
Kernel density estimation
Road sign recognition
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Summary:Driver support systems (DSS) of intelligent vehicles will predict potentially dangerous situations in heavy traffic, help with navigation and vehicle guidance and interact with a human driver. Important information necessary for traffic situation understanding is presented by road signs. A new kernel rule has been developed for road sign classification using the Laplace probability density. Smoothing parameters of the Laplace kernel are optimized by the pseudo-likelihood cross-validation method. To maximize the pseudo-likelihood function, an Expectation–Maximization algorithm is used. The algorithm has been tested on a dataset with more than 4900 noisy images. A comparison to other classification methods is also given.
ISSN:0167-8655
1872-7344
DOI:10.1016/S0167-8655(00)00078-7