Fast Background Subtraction and Shadow Elimination Using Improved Gaussian Mixture Model

Abstract

Background subtraction is widely used to detect moving object from static cameras. It is usually regard as one of the most important step in applications such as traffic monitoring, human motion capture and recognition, video surveillance, etc. In order to get a good performance of the whole system, the background subtraction method could not be so time and space consuming, and the accuracy is also required. Gaussian mixture model is a robust background subtraction method and is widely used ever since it is proposed. Some of the shortcomings of this model such as slow updating rate, slow initialization procedure and time and space consuming can be seen in some literatures and the corresponding resolution methods are also proposed. In this paper, an improved Gaussian mixture model is proposed to save time and space. New shadow detection and noise removing method are also proposed. the accuracy is also required.