A texture-based approach for shadow detection

Shadows detection is a fundamental step in visual surveillance and monitoring systems. Shadow points are often misclassified as object points causing errors in localization, segmentation, measurements, tracking and classification of moving objects. In order to detect potential shadow points an automatic segmentation procedure is performed and, for all moving pixels, the approach evaluates the compatibility of photometric properties with shadow characteristics. The approach is improved using a new method focused on the similarity between little textured patches: this method is based on the observation that shadow regions present same textural characteristics in each frame of the gray-level video-sequence and in the corresponding adaptive background model. We suggest a new approach to describe textural information in terms of redundant functions. The algorithm is designed to be unaffected by scene type, background type or light conditions. Results validate the algorithm ´s performance on a benchmark suite of indoor and outdoor video sequences.