采用自适应高斯混合方法为背景建模的难点是对背景模型的维持与更新。
Taking the method of adaptive Gaussian mixture method can make model for background meanwhile it is a difficult point to maintain and update background model.
该算法使用混合高斯模型表示粒子，在每个时刻的修正步骤之后，采用EM算法对粒子进行重新拟合。
It USES Gassian mixture model to represent particles and adopts EM algorithm to refit particles after correction step at each time.
为了解决传统高斯混合模型（GMM）对初值敏感，在实际训练中极易得到局部最优参数的问题，提出了一种采用微粒群算法优化GMM参数的新方法。
The traditional training methods of Gaussian Mixture Model(GMM) are sensitive to the initial model parameters, which often leads to a local optimal parameter in practice.
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