Gaussian Mixture Modeling

However, the commonly used Gaussian mixture model and modeling based on kernel estimation both have their own shortcomings.

而通常所采用的混合高斯模型和基于核估计的方法具有各自的缺点。

Thus, the paper focuses on the following aspects. First, in the extraction of the target vehicle, the paper analyzes some commonly used methods which have Multi-frame averaging method, continuous frame difference method, optical flow method, Gaussian mixture modeling approach.

因而论文着重研究了以下几个方面。首先论文在对目标车辆提取方面，分析了一些常用的方法有多帧平均法、连续帧差法、光流法、高斯混合建模法等。

The improved algorithm replaces the tone reverse projection image of the target vehicle with the target vehicle binary image obtained by the Gaussian mixture modeling method.

改进的算法是利用混合高斯建模法得到的目标车辆二值图像，替换目标车辆跟踪的色调反向投影图像。

This article optimizes the Gaussian Mixture background modeling algorithm through in-depth study of it, improving the speed of updates, and almost does not change the accuracy.

通过对混过高斯背景建模算法的研究，优化了背景更新算法，在几乎不改变准确率的前提下提高了背景更新的速度，为进一步的操作提供了较好的基础。

Scalable Parameter Learning Algorithm for Gaussian Mixture Modeling

高斯混合模型的数据尺度可压缩参数学习算法

Structural risk minimization principle based gaussian mixture modeling

基于结构风险最小化准则的高斯混合模型的参数估计

We perform unsupervised classification to hyperspectral image by Gaussian mixture modeling, estimate the parameters of the mixture model using the expectation maximization ( EM) algorithm in merged operations to data after PCA linear projection, and select the number of components according to the proposed criterion.

分类过程中，论文采用期望最大化（Expectation maximization）算法在合并的策略下对PCA变换后的数据求解混合模型，并应用所提出的准则进行模型选择从而确定待分类的类别数。

Further programming of the above four algorithms, simulation results show that Gaussian mixture modeling algorithm is better and more conducive to the transport vehicle segmentation.

进一步通过编程实现上述四种算法，仿真实验结果表明，混合高斯建模算法的分割效果更好，更有利于交通车辆分割。

For all of the background modeling methods, the Gaussian mixture modeling is considered as a good method with high performance in both detection capability and adaptability. Nevertheless, it is difficult to implemented in real time for its huge computational complexity.

在背景建模各方法中，混合高斯建模方法是公认的检测效果和适应性都较好的方法，但其所需计算量巨大，难以实时实现。