Realize a mobile lane detection system based on pocket PC portable devices
ISCGAV'07 Proceedings of the 7th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
An Effective and Fast Lane Detection Algorithm
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Road Lane Detection with Elimination of High-Curvature Edges
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Lane detection using support vector machines
IMSA '07 Proceedings of the Eleventh IASTED International Conference on Internet and Multimedia Systems and Applications
An experimental study on object recognition and enhancement in MWIR thermography
International Journal of Knowledge Engineering and Soft Data Paradigms
Dynamic Tracking System through PSO and Parzen Particle Filter
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
An adaptive method for detecting lane boundary in night scene
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Keeping the vehicle on the road: A survey on on-road lane detection systems
ACM Computing Surveys (CSUR)
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Three-feature based automatic lane detection algorithm (TFALDA) is a new lane detection algorithm which is simple, robust, and efficient, thus suitable for real-time processing in cluttered road environments without a priori knowledge on them. Three features of a lane boundary - starting position, direction (or orientation), and its gray-level intensity features comprising a lane vector are obtained via simple image processing. Out of the many possible lane boundary candidates, the best one is then chosen as the one at a minimum distance from the previous lane vector according to a weighted distance metric in which each feature is assigned a different weight. An evolutionary algorithm then finds the optimal weights for combination of the three features that minimize the rate of detection error. The proposed algorithm was successfully applied to a series of actual road following experiments using the PRV (POSTECH research vehicle) II both on campus roads and nearby highways.