Combining Statistical Hough Transform and Particle Filter for robust lane detection and tracking

Lane detection and tracking is still a challenging task. Here, we combine the recently introduced Statistical Hough transform (SHT) with a Particle Filter (PF) and show its application for robust lane tracking. SHT improves the standard Hough transform (HT) which was shown to work well for lane detection. We use the local descriptors of the SHT as measurement for the PF, and show how a new three kernel density based observation model can be modeled based on the SHT and used with the PF. The application of the former becomes feasible by the reduced computations achieved with the tracking algorithm. We demonstrate the use of the resulting algorithm for lane detection and tracking by applying it to images freed from the perspective effect achieved by applying Inverse Perspective Mapping (IPM). The presented results show the robustness of the presented algorithm.