Recursive 3-D Road and Relative Ego-State Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Computer Vision and Image Understanding
Multilocal creaseness based on the level-set extrinsic curvature
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lane following and lane departure using a linear-parabolic model
Image and Vision Computing
Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation
IEEE Transactions on Intelligent Transportation Systems
Crossing road monitoring system based on adaptive decision for illegal situation
Applied Soft Computing
Recent progress in road and lane detection: a survey
Machine Vision and Applications
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Detection of lane markings based on a camera sensor can be a low cost solution to lane departure and curve over speed warning. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue due to cast shadows, wearied and occluded markings, variable ambient lighting conditions etc. We focus on increasing the reliability of detection in two ways. Firstly, we employ a different image feature other than the commonly used edges: ridges, which we claim is better suited to this problem. Secondly, we have adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair or lane lines to the image features, based on both ridgeness and ridge orientation. In addition this fitting is performed for the left and right lane lines simultaneously, thus enforcing a consistent result. We have quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known.