Lane boundary detection using deformable templates: Effects of image subsampling on detected lane edges

Abstract

In order to be robust, a system for detecting road lane boundaries in images needs to be able to handle scenes which contain large amounts of clutter due to shadows, puddles, oil stains, skid marks, leaves and dirt on the road, etc. Many prior systems threshold the image gradient magnitude to detect edges, and then use the detected edge points to identify the lane boundaries. This can result in a very noisy edge image, as there are many situations in which there are strong edges in the image due to irrelevant clutter. There are also many situations in which the edges of the features defining portions of the lane boundaries have a lower intensity gradient than distracting clutter edges in the image. The LOIS (Likelihood of Image Shape) lane detection algorithm solves this problem by using a deformable template approach that uses image intensity gradient information in a way which does not require thresholding. This paper describes the LOIS algorithm in detail, and shows the results of applying the algorithm to a number of challenging scenes. Images are included comparing the lane boundaries detected by applying LOIS to a 240×256 image with the lane boundaries detected by applying the algorithm to a 30×32 subsampled version of the same image. Qualitatively the results are very similar, but the algorithm runs 45 times faster on the subsampled images.