Multiresolutional Gaussian Mixture Model for Precise and Stable Foreground Segmentation in Transform Domain
This paper describes a multiresolutional Gaussian mixture model (GMM) for precise and stable foreground segmentation. A multiple block sizes GMM and a computationally efficient fine-to-coarse strategy, which are carried out in the Walsh transform (WT) domain, are newly introduced to the GMM scheme. By using a set of variable size block-based GMMs, a precise and stable processing is realized. Our fine-to-coarse strategy comes from the WT spectral nature, which drastically reduces the computational steps. In addition. the total computation amount of the proposed approach requires only less than 10% of the original pixel-based GMM approach. Experimental results show that our approach gives stable performance in many conditions. including dark foreground objects against light, global lighting changes, and scenery in heavy snow.
gaussian mixture model foreground segmentation fine-to-coarse strategy walsh transform variable block size