Automatic road sign detection and recognition

Road Sign Detection and Recognition (RSDR) systems provide an additional level of driver assistance, leading to improved safety for passengers, road users and vehicles. As part of Advanced Driving Assistance Systems (ADAS), RSDR can be used to benefit drivers (specially with driving disabilities) by alerting them about the presence of road signs to reduce risks in situations of driving distraction, fatigue ,poor sight and weather conditions. Although a number of RSDR systems have been proposed in literature; the design of a robust algorithm still remains an open research problem. This thesis aims to resolve some of the outstanding research challenges in RSDR, while considering variations in colour illumination, scale, rotation, translation, occlusion, computational complexity and functional limitations. RSDR pipeline is divided into three parts namely; Colour Segmentation, Shape Classification and Content Recognition. This thesis presents each part as a separate chapter, except for Colour Segmentation that introduces two distinct approaches for Road Sign region of interest (ROI) selection. The first approach in Colour Segmentation presents a detailed investigation of computer based colour spaces i.e. YCbCr, YIQ, RGB, CIElab, CYMK and HSV, whereas second approach presents the development and utilisation of an illumination invariant Combined Colour Model (CCM) on Gamma Corrected images containing road signs considering varying illumination conditions. Shape Classification of the road sign acts as second part of RSDR pipeline consisting on shape feature extraction and shape feature classification stages. Shape features of road signs are extracted by introducing Contourlet Transforms at the decomposition level-3 with haar filters for generating the Laplacian Pyramid (LP) and Directional Filter Bank (DFB). The third part of the RSDR system presented in this thesis is the Content Recognition, which is carried out by extracting the LESH (Local Energy based Shape Histogram) features of the normalized road sign contents. Extracted shape and content features are utilised to train a Support Vector Machine (SVM) polynomial kernel which are later classified with the input candidate road sign shapes and contents respectively. The thesis further highlights possible extensions and improvements to the proposed approaches for RSDR.