Hierarchical Additive Hough Transform for Lane Detection

Detection of lanes is an important problem in the upcoming field of vehicle safety and navigation, for which linear Hough transform (HT) is widely used. In order to meet real-time requirements, various attempts to accelerate the HT have been proposed in the past, including hierarchical HT. Existing hierarchical approaches involve the overhead of recomputing HT values at every level in the hierarchy. In this letter, we propose a novel, computationally efficient hierarchical HT by extending and applying the additive property of HT to multiple levels of hierarchies. This proposed approach, called hierarchical additive Hough transform (HAHT) is shown to lead to significant computational savings of up to 98-99% in the Hough voting process. The HAHT has been validated on a wide range of straight lane images and it is shown to successfully detect lanes.