Gaussian Mixture Background Model Based on Entropy Image and Membership-Degree-Image
基于熵图像和隶属度图的高斯混合背景模型

The number of Gaussian component is fixed and correlativity of class label between adjacent pixels is not considered in classical Gaussian mixture background model. As an improved version of the model, the main contribution of this paper is twofold. The first is to construct entropy image to measure the complexity of pixel’s intensity distribution, and further present the adaptation mechanism for automatically choosing the component number of Gaussian mixture model for each pixel according to entropy image so that the computational cost can be reduced without significantly sacrificing detection accuracy. The other is to use the membership degree to measure the degree that one pixel belongs to the background, and further fusion the local information within its adjacent region for effective pixel classification so that the classification decision becomes more reliable without significantly increasing the computation load. Experiments conducted on various real scenes demonstrate the good performance in computational speed and accuracy.