Detection of curved road edges in radar images via deformable templates

Three methods of detecting road edges in millimeter-wave radar images are presented. All of them are based on deformable template priors and random field likelihoods. The first method is formulated in a Bayesian setting and employs an adaptive MAP estimate. The second method is a modification of the first, using a novel weighting scheme. The third method is based on a three-region indicator matrix which is used to impose the non-linear constraints implicit on road geometry via addition of a sum of quasi-quadratic matrix forms to the log-normal likelihood. Unlike the first two methods, that employ the Metropolis algorithm to find the optimal road edges, the third method uses a deterministic recursive scheme designed to find the optimal indicator matrix. Experimental results are presented to show the advantages of these methods.