Shadow removal with blob-based morphological reconstruction for error correction

Dealing with shadows and highlights is essential in object detection and tracking applications such as automated video surveillance systems. This is especially true for outdoor scenarios subject to variable lighting and weather conditions. In this paper, we present a novel scheme for effective shadows (highlights) detection using both color and texture cues. Since in any such algorithm, misclassifications often occur, resulting in distorted object shapes, the core of this scheme is the introduction of a technique capable of correcting these errors. The technique is based on morphological reconstruction of the shadow-removed blobs conditioned on the blobs prior to a shadow-removal process, assuming that the object shapes are properly defined along most part of their contours after the initial detection. Experiments on a variety of real-world video data demonstrate the favorable performance and robustness of the proposed scheme.