Abstract:
Automatic detection and recognition of road signs is an important component of automated
driver assistance systems contributing to the safety of the drivers, pedestrians and vehicles.
Despite significant research, the problem of detecting and recognizing road signs still remains
challenging due to varying lighting conditions, complex backgrounds and different viewing
angles. We present an effective and efficient method for detection and recognition of traffic signs
from images. Detection is carried out by converting the image to HSV color space and applying
a color based segmentation. A set of geometrical constraints is then applied to the segmented
regions. The final region of interest is extracted by applying Hough transform to the image which
searches for a circle, triangle or a rectangle in the image. Once the sign is detected, recognition is
carried out using three different state-of-the-art feature matching techniques. These include Scale
Invariant Feature Transform (SIFT), Speed up Robust Features (SIFT) and Binary Robust
Invariant Scalable Key points (BRISK). The proposed methodology evaluated on a data set of
frequently occurring road signs reported promising results on detection as well as recognition. A
comparative analysis of the three descriptors reveal that while SIFT achieves the best recognition
rates, BRISK is the most efficient of the three descriptors in terms of computation time.
