Effective moving cast shadow detection for monocular color image sequences

Abstract

For an accurate scene analysis in monocular image sequences, a robust segmentation of a moving object from the static background is generally required. However, the existence of moving cast shadow may lead to an inaccurate object segmentation, and as a result, lead to further erroneous scene analysis. An effective detection of moving cast shadow in monocular color image sequences is developed. Firstly, by realizing the various characteristics of shadow in luminance, chrominance, and gradient density, an indicator, called shadow confidence score, of the probability of the region classified as cast shadow is calculated. Secondly the canny edge detector is employed to detect edge pixels in the detected region. These pixels are then bounded by their convex hull, which estimates the position of the object. Lastly, by analyzing the shadow confidence score and the bounding hull, the cast shadow is identified as those regions outside the bounding hull and with high shadow confidence score. A number of typical outdoor scenes are evaluated and it is shown that our method can effectively detect the associated cast shadow from the object of interest.