Road sign classification using Laplace kernel classifier

Driver support systems 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 ExpectationMaximization algorithm is used. The algorithm has been tested on a dataset with more than 4 900 noisy images. A comparison to other classification methods is also given. Key words: Road sign recognition, Kernel density estimation, Expectation-maximization algorithm

• Type: Journal
• Year: 2000
• Where: PRL
• Authors: Pavel Paclík, Jana Novovicová, Pavel Pudil, Petr Somol
• Keywords: Driver Support Systems, Laplace Probability Density, PRL 2000, Road Sign