Motion Objects Segmentation and Shadow Suppressing without Background Learning

Abstract

An approach to segmenting motion objects and suppressing shadows without background learning has been developed. Since wavelet transformation indicates the position of sharper variation, it is adopted to extract the information contents with the most meaningful features based on two successive video frames only. According to the fact that the saturation component is lower in the region of shadow and is independent of the brightness, HSV color space is selected to extract foreground motion region and suppress shadow instead of other color models. A local adaptive thresholding approach is proposed to extract initial binary motion masks based on the results of the wavelet transformation. A foreground reclassification is developed to get an optimal segmentation by fusion of mode filtering, connectivity analysis, and spatial-temporal correlation. Comparative studies with some investigated methods have indicated the superior performance of the proposal in extracting motion objects and suppressing shadows from cluttered contents with dynamic scene variation and crowded environments.