Lane detection of multi-visual-features fusion based on D-S theory
作者： Junzheng Wang , Huayao Chang , Chao Chen , Jing Li
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摘要: A novel lane detection algorithm based on multi-visual-features fusion by using D-S evidence theory is introduced to improve the robustness against illumination variations, shadows and road surface cracks, etc. First, gradient magnitude, direction, hue value operators are chosen construct bodies, for which basic probability assignment functions designed respectively. Then, after pretreatment of conflict focal elements, evidences combined obtain weights each pixel as candidate points according maximum reliability criterion. Finally, parameters piecewise linear model calculated weighted Hough transform with constraint KF used tracking. The experimental results show that this method can achieve higher adaptability than simply edge or color feature, satisfies real-time requirement navigation.
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