利用混合高斯模型进行前景检测的源代码实现，依据的是Stauffer发表的Adapptive background mixture models for real-time tracking.-The prospects for the use of Gaussian mixture model, detection of the source code implementation, based on the Stauffer published Adapptive background mix

stauffer的经典的混合高斯模型算法描述，适合做行为检测的人使用。-stauffer the classical Gaussian mixture model algorithm descr iption, suitable for people who use behavior detection.

背景建模方法之高斯混合模型，使用到MOG2。算法快，并且可以进行阴影检测。遍历性：对每一个像素进行建模。作者为Z.Zivkovic-The algorithm similar to the standard Stauffer&Grimson algorithm with additional selection of the number of the Gaussian components based on: "Recursive unsupervised learning of fini

My implementation of Stauffer & Grimson's GMM(Background subtraction Drawbacks of Stauffer & Grimson's GMM Since its pixel based model fitting, a) lots of unwanted small blobs are scattered all over the frame. e.g. UCSD demo b) main blobs