提出了一种用于视觉监视系统的基于柯西分布的发光模型的光照不变变化检测方法。假定视频图像序列中每个背景图像像素点灰度观测值的时序变化由白噪声引起,利用建立的初始化背景高斯统计模型对每帧图像进行归一化,得到了背景像灰度比值的分布符合标准柯西分布的结论,解决了柯西分布的模型参量估计问题。在变化检测的基础上,YCbCr颜色空间的亮度、色调和饱和度被用来识别和消除由阴影和反光等引起的变化区域。结果表明,提出的背景建模方法对场景中各种光线变化、小的背景扰动等噪声具有稳健性,可以较为可靠地检测前景目标,识别和去除阴影和反光。

A novel illumination-invariant change detection method of shading model based on Cauchy distribution for visual surveillance systems is proposed. It is assumed that the observed temporal intensity variation of each pixel in background images is caused by white noise. After each image being normalized by an initialized Gaussian background model, the distribution of the intensity ratios between corresponding pixels of two background images obeys a Cauchy distribution. The parameter estimation of the Cauchy distribution model is simplified. Based on the change detection, the intensity, hue, and saturation in the YCbCr color space are employed to recognize and eliminate shadows and reflections in video sequences. The experimental results demonstrate that the proposed method of background modeling can tolerate the whole or local sudden or slow changes in illumination, and noises caused by some small motions, shadows or reflections in a background scene.