Combined dynamic tracking and recognition of curves with application to road detection

We present an algorithm that extracts the largest shape within a specific class, starting from a set of image edgels. The algorithm inherits the best-first segmentation approach. However, instead of being applicable only to shapes defined within a given class of curves, we have extended our approach to tackle more general-and complex-shapes. For example, we can now process shapes obtained from sets defined over different kinds of curves and related to one another by estimated parameters. Therefore, we go from a segmentation problem to a recognition problem. In order to reduce the complexity of the searching algorithm, we work with a linearly parameterized class of shapes. This allows us, first, to use a recursive least-squares fitting, second, to cast the problem as the search of a largest edgel subset in a directed acyclic graph, and, third, to easily introduce a priori information on the location of the edgels of the searched subset. This leads us to propose a unified approach where recognition and tracking are combined. Experiments on recognizing and tracking both left and right road boundaries demonstrate that real-time processing is achievable