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Horizontal roadway curvature data are essential for roadway safety analysis. However, collecting such data is time-consuming, costly, and dangerous using traditional, manual surveying methods. It is especially difficult to perform such manual measurement when roadways have high traffic volumes. Thus, it would be valuable for transportation agencies if roadway curvature data could be computed from photographic images taken using low-cost digital cameras. This is the first article that develops an algorithm using emerging vision technology to acquire horizontal roadway curvature data from roadway images to perform roadway safety assessment. The proposed algorithm consists of four steps: (1) curve edges image processing, (2) mapping edge positions from an image domain to the real-world domain, (3) calibrating camera parameters, and (4) calculating the curve radius and center from curve points. The proposed algorithm was tested on roadways having various levels of curves and using different image sources to demonstrate its capability. The ground truth curvatures for two cases were also collected to evaluate the error of the proposed algorithm. The test results are very promising, and the computed curvatures are especially accurate for curves of small radii (less than 66 m/200 ft) with less than 1.0% relative errors with respect to the ground truth data. The proposed algorithm can be used as an alternative method that complements the traditional measurement methods used by state DOTs to collect roadway curvature data. [ABSTRACT FROM AUTHOR]

This article presents a two-dimensional (2-D) color, shape, and texture-based stop sign recognition algorithm. The theoretical base of establishing a suitable 2-D correlation coefficient threshold value that can both eliminate nonstop sign objects and minimize the possibility of false-positive identifications is presented. Both the one-dimensional (1-D) algorithm, which has been developed by the authors, and the 2-D algorithm provide a simple and feasible means to automatically extract stop signs from roadway inventory video images with a very competitive computation speed of less than 25 ms for processing a 400 × 300 pixel image. 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 type of images that can be correctly detected by the 2-D algorithm but not the 1-D algorithm is significant because there is a need to recognize the tilted stop signs. The 2-D algorithm can detect stop signs tilted up to 35°. Similarly, the 2-D algorithm can detect a stop sign with its surface being 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. This algorithm can be extended to recognize other signs, such as yield signs, with a slight modification. [ABSTRACT FROM AUTHOR]