Detection, Categorization and Recognition of Road Signs for Autonomous Navigation

In this paper we present a novel and robust approach for detection, categorization and recognition of road signs. It is known that the standard road signs contain few and easily distinguishable colors, such as red for prohibition, yellow for warnings, green, blue and white. We use a Bayesian approach for detecting road signs in the captured images based on their color information. At the same time, the results of the Bayes classifier categorize the detected road sign according to its color content. The SIFT transform is employed in order to extract a set of invariant features for the detected road sign label(s). Recognition is done by matching the extracted features with previously stored features of standard signs. We illustrate the accuracy and robustness of this approach.