Traffic sign detection based on visual co-saliency in complex scenes

Abstract

Co-saliency detection aims at finding the salient regions from multiple images which capture the focus of human visual system. In this paper, a novel visual co-saliency algorithm is proposed, which adopts three human visual attention cues: contrast, center-bias and symmetry. In order to apply co-saliency to the detection of traffic signs, a traffic sign detection framework based on visual co-saliency in complex scenes is devised. The detection process involves two stages. In the first stage, a cluster-based co-saliency model is built to generate the final co-saliency map. In the second stage, a geometric structure constraint model is constructed to discriminate the detected salient objects and then accurately achieve location of traffic signs. The advantage of our approach lies in the integration of bottom-up and top-down visual processing, and no heavy learning tasks. Experiments on a variety of benchmark databases illustrate high precision, high recall and operation efficiency of the proposed approach. Besides, for traffic sign detection it overcomes the interference of complex urbanization backgrounds. Furthermore, the best trade-off between precision and recall on warning signs is achieved, reaching 93.30% and 89.06%, respectively.