Abstract
Vision-based systems for finding road lanes have to operate robustly under a wide variety of environmental conditions including large amount of scene clutters. This paper presents a method to find the lane boundaries by combining a local line extraction method and dynamic programming. The line extractor obtains an initial position of road lane boundaries from the noisy edge fragments. Then, dynamic programming improves the initial approximation to an accurate configuration of lane boundaries. Input image frame is divided into sub-regions along the vertical direction. The local line extractor extracts candidate lines of road lanes in the sub-region. Most prominent lines are found among candidate lines by dynamic programming that minimizes the functional which measures the deviation from a virtual straight line. The search framework based on DP method reduces computational cost. Experimental results using images of real road scenes demonstrate the feasibility of the proposed algorithm.
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