Occlusion‐robust traffic sign detection via cascaded colour cubic feature

Abstract

The high variability of sign appearance with partial occlusions in uncontrolled environments has made the detection of traffic signs a challenging problem in computer vision. In this study, an occlusion‐robust traffic sign detection framework is proposed. To achieve occlusion‐robust detection, a colour cubic feature called colour cubic local binary pattern (CC‐LBP) is proposed to construct a coarse‐to‐fine cascaded detector. The CC‐LBP utilises colour information and a self‐adaptive threshold to express multiclass traffic signs, which can effectively remove non‐object subwindows in the cascade‐based detection. The verification experiments show that the proposed CC‐LBP feature performs better than the previous rectangular features in representing multiclass traffic signs, and that the proposed occlusion‐robust detection method can detect multiclass partial occluded traffic signs with high accuracy in real time.