针对背景减除法中经典混合高斯模型计算量过大的问题，提出一种新的背景混合高斯模型。该方法利用偏差均值作为判断模型是否与当前像素值匹配的阈值参数，有效减少了经典模型中由于开平方及指数运算带来的庞大计算量；同时引入持续平稳时间的概念，采用非线性权值更新方法，能够使较长时间停留在场景中的物体迅速成为背景。实验结果表明，该方法显著提高了背景模型的计算效率。

A deficit of classic Gaussian mixture model in background subtraction is the high computation cost. To solve this problem, a novel algorithm is proposed in this paper. A threshold parameter corresponding to the mean of deviation is utilized to judge whether a model matches the current pixel intensity. The new algorithm efficiently reduces the calculation burden of the operation of square and exponent with classical model; meanwhile, a non-linear weight updating approach is adopted, with the notion of sustain stationary time introduced in, and hence the quick converting of a relative long still object in scene into the background is achievable. Experimental results show that the new algorithm has significantly improved the calculation efficiency of background model.