针对户外视频监控存在光照变化这一问题, 提出一个用于准确完成目标检测的实时背景建模框架. 考虑到目标检测

的准确性要求, 建立基于帧间像素亮度差统计直方图的像素亮度扰动阈值. 在此基础上, 针对背景建模的实时性要求, 提出一种基于自回归背景模型的参数快速更新方法. 鉴于不同光照变化的适应性要求, 定义对光照变化不敏感的背景纹理模型. 上述模型统称为自回归{ 纹理(Auto regression and texture, ART) 模型, 该模型适应于户外光照变化. 基于该模型构建像素亮度和纹理置信区间用于目标检测. 实验结果表明, 该框架能适应和实时跟踪户外背景的光照变化, 并对目标进行准确检测.-Considering the appearance of illumination variation in outdoor video surveillance, a real-time background

modeling framework, which is also composed of accurate foreground detection, is established. In view of the accuracy

of foreground detection, a threshold based on the histogram of pixel0s intensity di® erence between neighboring frames is

proposed. On account of the real-time background modeling, a fast estimation approach on parameters of autoregressive

model is presented. Considering the adaptability to variable illumination, a texture background model insensitive to

outdoor illumination variation is designed. Thus, a uniform model named auto regression and texture (ART) is obtained.

According to the established con¯ dence intervals with perturbation of pixel s intensity and its local texture, foreground in

scenes with di® erent illumination variations is successfully detected. The experimental results indicate that the framework

is adaptive to and can exactly