Novel two-stage algorithm for non-parametric cast shadow recognition

Environment perception and scene understanding is an important issue for modern driver assistance systems. However, adverse weather situations and disadvantageous illumination conditions like cast shadows have a negative effect on the proper operation of these systems. In this paper, we propose a novel approach for cast shadow recognition in monoscopic color images. In a first step, shadow edge candidates are extracted evaluating binarized channels in the color-opponent and perceptually uniform CIE L*a*b* space. False detections are rejected in a second verification step, using SVM classification and a combination of meaningful color features. We introduce a non-parametric representation for complex shadow edge geometries that enables utilizing shadow edge information for improving downstream vision-based driver assistance systems. A quantitative evaluation of the classification performance as well as results on multiple real-world traffic scenes show a reliable cast shadow recognition with only a few false detections.