中文说明：高斯模型就是用高斯概率密度函数（正态分布曲线）精确地量化事物，将一个事物分解为若干的基于高斯概率密度函数（正态分布曲线）形成的模型。 对图像背景建立高斯模型的原理及过程：图像灰度直方图反映的是图像中某个灰度值出现的频次，也可以以为是图像灰度概率密度的估计。如果图像所包含的目标区域和背景区域相差比较大，且背景区域和目标区域在灰度上有一定的差异，那么该图像的灰度直方图呈现双峰-谷形状，其中一个峰对应于目标，另一个峰对应于背景的中心灰度。对于复杂的图像，尤其是医学图像，一般是多峰的。通过将直方图的多峰特性看作是多个高斯分布的叠加，可以解决图像的分割问题。 在智能监控系统中，对于运动目标的检测是中心内容，而在运动目标检测提取中，背景目标对于目标的识别和跟踪至关重要。而建模正是背景目标提取的一个重要环节。
English Description：
Gaussian model is a Gaussian probability density function (bell curve) to accurately quantify things, breaking a thing based on Gaussian probability density function (bell curve) formation models. Principle and process of the Gaussian model of image background: image histogram is a reflection of a gray value appears in the image frequency can also be thought image grey level probability density estimation. If the image contains a relatively large difference in target regions and the background area, and the target region on a gray background area and there are some differences, the image histogram shape of peak-Valley, one peak corresponds to the destination, another peak corresponds to the center of the background grayscale. For complex images, especially medical images, typically peak. By multimodal character of the histogram as a superposition of multiple Gaussian distribution, image segmentation problems can be solved. Intelligent monitoring systems, for moving object detection is