Road sign classification using Laplace kernel classifier

doi:10.1016/S0167-8655(00)00078-7

Road sign classification using Laplace kernel classifier

2000-12-01

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DOI：10.1016/S0167-8655(00)00078-7

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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. (C) 2000 Elsevier Science B.V. All rights reserved.

Keyword:

road sign recognition

kernel density estimation

expectation-maximization algorithm

Road sign classification using Laplace kernel classifier

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