改进的混合高斯自适应背景模型

An improved Gaussian mixture model for an adaptive background model

混合高斯背景模型是背景建模领域最常用的构建算法,针对该方法在实际应用中的缺陷,提出了2点改进措施:像素过滤方法和按背景演变过程进行划分的自适应学习率方法.像素过滤方法记录某点像素值在一个短时间段内的变化情况,对其进行统计分析,根据均值和方差过滤掉快速运动目标的动态干扰像素,增强算法的鲁棒性;新的自适应学习率方法将背景的形成过程划分为4个阶段,对不同的阶段使用不同的学习率,加速背景的形成和消退.应用改进后的算法在两段街道监控视频中同原算法进行了对比实验.实验结果表明,改进方法在视觉效果上有着显著提高,背景形成迅速、清晰.改进方法增强了算法的抗干扰能力,提高了背景的形成和切换速度,可以作为基础算法应用于相关视觉处理之中.

The Gaussian Mixture Model（GMM） has been widely used for modeling backgrounds.Aiming to overcome the defect in practical application,the classical algorithm was improved in two ways.The pixel filtering method and the new adaptive learning rate method were presented.The pixel filtering method recorded the pixel value of a point in a short period of time,and then analyzed this data.According to the pixel mean and variance,the dynamic interfering pixels of fast moving targets can be filtered out.The formation of the background was divided into four stages in the new adaptive learning rate method.Additionally,different stages were assigned different learning rates,which can speed up the background of the formation and regression.Compared with the original algorithm,the experimental results showed that the improved algorithm has a good visual effect in the surveillance video of two streets,and the background forms more quickly and clearly.The improved method,which can be applied to other visual processing algorithms,enhances the robustness and accelerates the formation of the background.