Improved moving objects detection method based on Gaussian mixture model
改进的基于高斯混合模型的运动目标检测方法

This paper proposed an improved moving objects detection method based on Gaussian mixture model in the case of focusing on a video monitoring system with a static camera. First, for updating the parameters (mean and variance) of the Gaussian models, the learning rates of mean and variance were different: for mean, the learning rate was adaptive, while for variance, the learning rate was fixed; Second, The notion of Mean Of the Weight (MOW) was introduced, which had a big contribution for differentiating background points from foreground points. Third, Shadow was detected and removed with the help of background image. Experimental results show that the proposed method possesses better ability of learning and higher efficiency of detecting large and slow objects in busy environments.