Robust Traffic Sign Recognition Based on Color Global and Local Oriented Edge Magnitude Patterns

Abstract

Most of the existing traffic sign recognition (TSR) systems make use of the inner region of the signs or the local features such as Haar, histograms of oriented gradients (HOG), and scale-invariant feature transform for recognition, whereas these features are still limited to deal with the rotation, illumination, and scale variations situations. A good feature of a traffic sign is desired to be discriminative and robust. In this paper, a novel Color Global and Local Oriented Edge Magnitude Pattern (Color Global LOEMP) is proposed. The Color Global LOEMP is a framework that is able to effectively combine color, global spatial structure, global direction structure, and local shape information and balance the two concerns of distinctiveness and robustness. The contributions of this paper are as follows: 1) color angular patterns are proposed to provide the color distinguishing information; 2) a context frame is established to provide global spatial information, due to the fact that the context frame is established by the shape of the traffic sign, thus allowing the cells to be aligned well with the inside part of the traffic sign even when rotation and scale variations occur; and 3) a LOEMP is proposed to represent each cell. In each cell, the distribution of the orientation patterns is described by the HOG feature, and then, each direction of HOG is represented in detail by the occurrence of local binary pattern histogram in this direction. Experiments are performed to validate the effectiveness of the proposed approach with TSR systems, and the experimental results are satisfying, even for images containing traffic signs that have been rotated, damaged, altered in color, or undergone affine transformations or images that were photographed under different weather or illumination conditions.