A Vision-Based Lane Detection System Combining Appearance Segmentation and Tracking of Salient Points
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Reliable lane detection is a key component of autonomous vehicles supporting navigation in urban environments. This paper introduces the GOLDIE (Geometric Overture for Lane Detection by Intersections Entirety) system, a vision-based software architecture that uses an on-board single camera to determine the position of road lanes with respect to the vehicle. We propose an efficient vision-based lane-detection system that combines an appearance-based analysis with salient point tracking. The appearance-based analysis consists of segmenting high contrast areas that fit inside a Region-Of-Interest (ROI) on the frame. The salient point tracker selects interesting points based in a reference line, that guides a dynamic ROI. The tracking ROI look for paint lane marks close to the last lane reference found, where road marks are likely to emerge, in order to maintain the usability of the salient point tracker. The tracking is performed with the Lucas-Kanade algorithm and the lane points candidates are selected according to a predefined triangular model. Once such lanes points are detected, the vehicle position is estimated based on the intersection of linearised lanes determined through a vanishing point approach. Experiments and comparisons with other algorithms illustrate the applicability of the method.
Intelligent Vehicles Symposium (IV), 2013 IEEE
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