Median mixture model for background – foreground segmentation in video sequences

Abstract

The purpose of this paper is to present a novel approach to the Gaussian mixture background modeling model (GMM) that we call the median mixture model (MMM). The proposed method is based on the same principles as the GMM, but all of the background model parameters are estimated in a much more efficient way resulting in accelerating the algorithm by about 25% without deteriorating the modeling results. The second part of this paper describes a method of uniting three MMMs where three different sets of input data undergo modeling in order to achieve even better results. This approach called the united median mixtures is more robust to random noise as well as unwanted shadows and reflections. Both algorithms are thoroughly tested and compared against the Gaussian mixture model, taking into consideration robustness to noise, shadows and reflections.