为了更好地进行视觉监视，该文给出了一种新的基于柯西分布的光照不变的统计变化检测算法。该算法首先将两帧图像间的灰度比值作为背景建模和剔除的特征，并且在假定背景图像中，当每个像素点观测的时序灰度变化由白噪声引起时，两帧背景图像中对应像素间的灰度比值的分布符合柯西分布；然后基于该变化检测方法，将YCbCr颜色空间的亮度、色调和饱和度用来识别和消除视频序列图像中的阴影。实验结果表明，该新算法不仅可以承受整体或局部的、缓慢或突然的光线变化，并且可以滤除由场景背景中小的扰动而导致的噪声。

A novel illumination-invariant change detection algorithm based on Cauchy distribution is proposed. The intensity ratios between two images are used as the feature to model and subtract background. The distribution of the intensity ratios between corresponding pixels of two background images follows Cauchy distribution, assuming that some observed temporal intensity variation of each pixel in background images are caused by white noise. The intensity, hue and saturation in the YCbCr color space are employed to recognize and eliminate shadows in video sequences. Finally, experimental results demonstrate that the proposed algorithm can tolerate the whole or local quick or slow changes in illuminations, and can filter noise caused by small motion in scene background.