Object-background segmentation from video

Abstract

Fast and accurate algorithms for background-foreground separation are an essential part of any video surveillance system. GMM (Gaussian Mixture Models) based object segmentation methods give accurate results for background-foreground separation problems but are computationally expensive. In contrast, modeling with only single Gaussian improves the time complexity with the reduction in the accuracy due to variations in illumination and dynamic nature of the background. It is observed that these variations affect only a few pixels in an image. Most of the background pixels are unimodal. We propose a method to account for the dynamic nature of the background and low lighting conditions. It is an adaptive approach where each pixel is modeled as either unimodal Gaussian or multimodal Gaussians. The flexibility in terms of number of Gaussians used to model each pixel, along with learning when it is required approach reduces the time complexity of the algorithm significantly. To resolve problems related to false negative due to the homogeneity of color and texture in foreground and background, a spatial smoothing is carried out by K-means, which improves the overall accuracy of proposed algorithm. The shadow causes the problem in many applications which rely on segmentation results. Shadow cause variation in RGB values of pixels, RGB value dependent GMM based method can’t remove shadow from detection results. The preprocessing stage involving illumination invariant representation takes care of the object shadow as well.