Abstract

Traffic sign recognition is one of the main components of intelligent transportation systems (ITS). It improves safety by informing the driver of the current state of the road, e.g., warnings, prohibitions, restrictions, and other information useful for driving. This paper presents a new road sign recognition method that is achieved in three main steps. The first step maps the input image from the Cartesian coordinate system to the log-polar one. The second step computes the histogram of oriented gradients, local binary pattern, and local self-similarity characteristics from the image represented in the log-polar coordinate system. The third step performs classification on the basis of the random forest classifier and the features computed in the second step. The proposed method has been tested on the German Traffic Sign Recognition Benchmark dataset, and the results obtained are satisfactory when compared to the state-of-the-art approaches.

© 2018 Optical Society of America

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