Geometry Preserving Active Polygon-Incorporated Sign Detection Algorithm

A generalized traffic sign detection algorithm incorporating a hybrid active contour (HAC) model has been previously developed to automatically detect all types of traffic signs. Although the HAC model has shown some promising results, there are still some false negatives remaining due to the over-evolution of the HAC model. Therefore, further improvement is needed to reduce the number of these false negatives. This paper is aimed at developing a new geometry-preserving active polygon (GPAP) model to address the over-evolution issue in the HAC model for improving the computation speed. The contributions of this paper include (1) proposing a new geometry-preserving evolution that ensures that the "contour" only evolves at its vertices instead of at every point along the edges; (2) tailoring a new energy function that enables a polygon to effectively converge to traffic sign regions without losing its geometry integrity by using both local color contrast feature and global elongation and rectangularity features; (3) proposing a generalized Hough transform that can make an efficient initial guess for the active polygon based on the manual for uniform traffic control devices (MUTCD) sign shape template with a multiscale implementation. The experimental test shows that the proposed algorithm can achieve a per-sign detection rate of 85.6% out of 2,329 tested signs, while it can successfully avoid over-evolution and detect 233 traffic signs that could not be detected previously by the existing algorithm. By incorporating the proposed GPAP model into the existing generalized detection algorithm, the test on selected datasets with diverse image qualities, image contexts, and data acquisition configurations shows that the per-frame false negative rate is decreased by 12.2%; also, the average processing time reduces the existing algorithm's processing time by 50.2%. The proposed GPAP model shows the promise of improving both the accuracy and efficiency of the existing sign detection algorithm, which can be used to improve transportation agencies' image-based sign inventory. (c) 2014 American Society of Civil Engineers.