Regularised region-based Mixture of Gaussians for dynamic background modelling
Sriram Varadarajan, Hongbin Wang, Paul C. Miller, Huiyu Zhou · 2014
This paper introduces a momentum-like regularisation term for the region-based Mixture of Gaussians framework. Momentum term has long been used in machine learning, especially in backpropagation algorithms to improve the speed of convergence and subsequently their performance. Here, we prove the convergence of the online gradient method with a momentum term and apply it to background modelling by using it in the update equations of the region-based Mixture of Gaussians algorithm. It is then shown with the help of experimental evaluation on both simulated data and well known video sequences that these regularised updates help improve the performance of the algorithm.