Fast convergent Gaussian Mixture Model in moving objects detection

Background subtraction methods are widely exploited for moving objects detection in surveillance video sequences acquired by static camera. Gaussian Mixture Model (GMM), whose convergence speed is rather slow, can be used to model the background of complex scene. This paper adds virtual Gaussian component into GMM and optimizes the updating process of parameters in GMM, in order to increasing the convergence speed of GMM. Experimental results show that our method can detect moving objects in complex scene correctly with fast convergence speed.