Fast and Accurate Foreground Background Separation for Video Surveillance

摘要

Fast and accurate algorithms for background-foreground separation are 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 a single Gaussian improves the time complexity with a 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 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 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.