Background Subtraction with a Hierarchical Pitman-Yor Process Mixture Model of Generalized Gaussian Distributions

Abstract

This paper presents hierarchical Pitman-Yor process mixture of generalized Gaussian distributions for background subtraction. The motivation behind choosing generalized Gaussian distribution is its flexibility as compared to the widely used Gaussian. We also integrate the Pitman-Yor process into our proposed model for an infinite extension that leads to better performance in the task of background subtraction. Our model is learned via a variational Bayes approach and is applied on the challenging Change Detection dataset. Experimental results on background subtraction show the effectiveness of the proposed algorithm.