A fast approach for traffic panel detection from natural scene images

Abstract

Traffic panels contain rich text and symbolic information for transportation and scene understanding. Fast detection of traffic panels facilitates text information extraction but has been paid little attention by the community. In this paper, we propose a fast and robust approach for rectangular traffic panel detection from traffic scene images. Considering the rectangular shape of traffic panels, we first extract candidate line segments in gray-level image using a fast line segment detector, and panel proposals are formed from the line segments. The proposals are then filtered using a multi-stage classifier with multiple features. The surviving proposals undergo a post-processing refinement procedure using geometric constraints to give the bounding boxes of detected panels. Experimental results on real images from Baidu Street View demonstrate the effectiveness of the proposed method.