Johan Lindström
	
Background and Foreground Modelling Using an Online EM  Algorithm

Abstract

A novel approach to background/foreground segmentation using an online
{EM} algorithm is presented. The method models each layer as a
Gaussian mixture, with local, per pixel, parameters for the background
layer and global parameters for the foreground layer, utilising
information from the entire scene when estimating the
foreground. Additionally, the online EM algorithm uses a progressive
learning rate where the relative update speed of each Gaussian
component depends on how often the component has been observed. It is
shown that the progressive learning rate follows naturally from
introduction of a forgetting factor in the log-likelihood.

Segmentation of RGB videos, as well as of output from an edge
detector, is  compared to the results of another algorithm. Especially
for the edge  detector video performance increases dramatically.