Unifying visual saliency with HOG feature learning for traffic sign detection

Abstract

Traffic sign detection is important to a robotic vehicle that automatically drives on roads. In this paper, an efficient novel approach which is enlighten by the process of the human vision is proposed to achieve automatic traffic sign detection. The detection method combines bottom-up traffic sign saliency region with learning based top-down features of traffic sign guided search. The bottom-up stage could obtain saliency region of traffic sign and achieve computational parsimony using improved model of saliency-based visual attention. The top-down stage searches traffic sign in these traffic sign saliency regions based on the feature histogram of oriented gradient (HOG) and the classifier support vector machine (SVM). Experimental results show that, the proposed approach can achieve robustness to illumination, scale, pose, viewpoint change and even partial occlusion. The smallest detection size of traffic sign is 14times14, the average detection rate is 98.3% and the false positive rate is 5.09% in test image data set.