视频监视前景图像估计的盲源提取方法

Foreground estimation in video surveillance by blind source extraction

在视频图像运动检测的背景消减方法中,场景图像或帧可建模为前景图像和背景图像的叠加或线性混合。然而,实际中图像的背景和前景往往相关,常用的主成分分析和独立分量分析等方法难以实现准确提取。为此,将视频图像的前景提取建模为盲源提取问题,提出了一种基于均方交叉预测误差的盲源提取方法,可以从相关的源视频图像中提取期望的前景图像,并将该方法扩展应用于基于基本模型和特征背景模型的背景消减方案中。基于人工和实际视频的实验验证了盲源提取背景消减方法的可行性和有效性。

In video surveillance, one scene image/frame can be modeled as a superimposition or linear mixture of foreground visual contents and background contents. In the real world, however, the background and foreground are correlated to each other. Therefore, the foreground extraction cannot be well solved by the PCA(principle component analysis) and the ICA(independent component analysis) algorithms. The foreground extraction was modeled as a BSE(blind source extraction) problem. The MSCPE(mean square cross prediction error), one solution of BSE, was generalized to extract desired source signal which was correlated with other source signals. Then MSCPE BSE method was applied to the background subtraction schemes by using the basic model and eigen backgrounds method. Experimental results on artificial video shows the feasibility of MSCPE, and the real-world video experiments demonstrate its effectiveness.