2014 12th International Conference on Frontiers of Information Technology (FIT) (2014)
Dec. 17, 2014 to Dec. 19, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2014.68
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 performing color based segmentation followed by application of Hough transform to find circles, triangles or rectangles. Recognition is carried out using three state-of-the-art feature matching techniques, SIFT, SURF and BRISK. The proposed system evaluated on a custom developed dataset reported promising detection and recognition results. 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.
Roads, Image color analysis, Image segmentation, Shape, Feature extraction, Transforms, Training,BRISK, Road sign detection, Road sign recognition, Hough transform, SIFT, SURF
Zumra Malik, Imran Siddiqi, "Detection and Recognition of Traffic Signs from Road Scene Images", 2014 12th International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 330-335, 2014, doi:10.1109/FIT.2014.68