Lane detection and tracking is a significant component of vision-based driver assistance systems (DAS). Low-level image processing is the first step in such a component. This paper suggests three useful techniques for low-level image processing in lane detection situations: bird's-eye view mapping, a specialized edge detection method, and the distance transform. The first two techniques have been widely used in DAS, while the distance transform is a method newly exploited in DAS, that can provide useful information in lane detection situations. This paper recalls two methods to generate a bird's-eye image from the original input image, it also compares edge detectors. A modified version of the Euclidean distance transform called real orientation distance transform (RODT) is proposed. Finally, the paper discusses experiments on lane detection and tracking using these technologies. © 2010 Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering.
CITATION STYLE
Jiang, R., Terauchi, M., Klette, R., Wang, S., & Vaudrey, T. (2010). Low-level image processing for lane detection and tracking. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (Vol. 30 LNICST, pp. 190–197). https://doi.org/10.1007/978-3-642-11577-6_24
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