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1. TRAFFIC SIGN DETECTION VIA GRAPH-BASED RANKING AND
SEGMENTATION ALGORITHMS
ABSTRACT
The majority of existing traffic sign detection systemsutilize color or shape information,
but the methods remainlimited in regard to detecting and segmenting traffic signs froma complex
background. In this paper, we propose a novel graphbasedtraffic sign detection approach that
consists of a saliencymeasure stage, a graph-based ranking stage, and a
multithresholdsegmentation stage. Because the graph-based ranking algorithm with specified
color and saliency combines the information ofcolor, saliency, spatial, and contextual
relationship of nodes, it ismore discriminative and robust than the other systems in terms of
handling various illumination conditions, shape rotations, andscale changes from traffic sign
images. Furthermore, the proposedmultithreshold segmentation algorithm focuses on all
thenodes with a nonzero ranking score, which can effectively solveproblems such as complex
background, occlusion, various illuminationconditions, and so on. The results for three public
traffic sign sets show that our proposed approach leads to better performancethan the current
state-of-the-art methods. Moreover,the results are satisfactory even for images containing
trafficsigns that have been rotated or undergone occlusion, as well asfor images that were
photographed under different weather andillumination conditions.