Moving object detection based on improved Gaussian Mixture Models

In the course of the moving-object detection,the background models are crucially important for the object extraction,but the Mixture Gaussian Mode(lGMM) is one of popular methods in the background models.For the deficiency of the GMM,two improvements are proposed in this paper.Firstly,the blocking model is introduced,which can significantly improve the detection rate with the airspace information among the pixels considered,while the GMM bases on the pixels and it needs much larger operations for the high-resolution image.Secondly,the foreground will be turned into background so as to it disappears if the moving object stay on one position of the scene for a long time.The solution is to update the entire frame or just update the background according to the state of moving object.The results show that the method can not only improve the detection rate of the moving object without affecting identification and reduce some noise,but also effectively solve the problem that the object turn into the background.Thus it can maintain the continuity of moving object.