Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l 1  norm, we derived a new set of updating equations for l 1  regularized online MoG. It is shown empirically that l 1  regularized online MoG converge faster than the original online MoG
['Hongbin Wang', 'Paul C. Miller']
Regularized online Mixture of Gaussians for background subtraction