Complementary features for traffic sign detection and recognition

Traffic Sign Detection and Recognition is an important component of intelligent transportation systems. It has captured the attention of the computer vision community for several decades. In this paper, we propose a new traffic sign detection and recognition approach consisting of color segmentation, shape classification and recognition stages. In the first stage, the image is segmented using look-up tables and thresholding on the HSI color space. The second stage uses Distance to borders (DtBs) features and random forest classifier to detect circular, triangular and rectangular shapes among the segmented ROIs. The last stage consists in the recognition of the detected signs. It is performed using Random Forest as classifier and histogram of oriented gradients (HOG) together with local self-similarity (LSS) as features. Experimental results show that the proposed method achieves high recall, precision, and correct classification accuracy ratios, and is robust to various adverse situations.