Fast MOG (Mixture of Gaussian) Algorithm based on Predicting Model Parameters

Abstract

Variable calculation at all pixels in a mixture of Gaussians (MOG) is a major obstacle for applications related to background removal. This paper presents a fast MOG algorithm based on partial parameter prediction. The proposed algorithm is applied to a limited number of pixels in order to reduce computational costs while maintaining conventional performance, and the parameter prediction is conducted using only the previous values and simple constant subtractions. Experiment results show that the predicted parameters almost match, in terms of pixel-to-pixel comparisons, to the exactly computed parameters in order to determine a proper model. In particular, the fast algorithm can save more than 37% of the model’s parameter computations while retaining 99.89% and 96.62% image and object-unit accuracies, respectively. Furthermore, it is verified that the pixels of the lost object do not affect the MOG algorithms. Therefore, we believe that it can be a useful tool for fast application development based on MOG algorithms.