Enhanced Roadway Inventory Using a 2-D Sign Video Image Recognition Algorithm

As part of a project to produce a real-time road inventory data collection system, a two-dimensional (2-D) color, shape, and texture-based stop sign recognition algorithm was developed. The theoretical base of establishing a suitable 2-D correlation coefficient threshold value that can both eliminate objects that are not stop signs and minimize the possibility of false-positive identifications is presented. Both the one-dimensional (1-D) algorithm, which has been developed previously by the authors, and the 2-D algorithm provide a simple and feasible way to automatically extract stop signs from roadway inventory video images with a very competitive computation speed. The 2-D algorithm was able to correctly detect all of the images that were correctly detected by the 1-D algorithm. In addition, the 2-D algorithm could correctly detect two of the remaining five images that could not be correctly detected by the 1-D algorithm. The 2-D algorithm can detect stop signs tilted up to 35° and can detect a stop sign with its surface blocked up to 20%. The improved detectability, with only a marginally increased time in the image processing, suggests that the 2-D algorithm is a better algorithm than the 1-D algorithm for processing stop sign images from video logging of roadway inventory.