运动目标检测是视频监控系统需要解决的关键问题之一。背景差法是固定的单摄像头监控中常用的一种运动目标检测方法，其核心是背景的构造。提出了一种基于码书的背景构造方法，它能在有限的存储空间开销下使用长时间的图像序列估计背景模型。该方法首先对每一个像素点的抽样进行聚类分析，即构造初始的码书；然后根据背景模型的条件从中挑选出合格的码字构造背景码书；最后通过判断当前的像素值是否可以划归于背景码书以区分背景点和前景点，同时做相应的更新。实验结果表明，即使背景本身存在运动和光照条件发生变化，该方法构造的背景也能有效检测运动目标。

Moving target detection is one of the critical issues in video surveillance. Background subtraction is a typical approach to detect moving targets in surveillance using only one static camera. The performance of background subtraction algorithms depends on how to construct background model. In this paper, codebook is used to estimate the background model using a long clip under limited memory. First, an initialization codebook is constructed for each pixel. Second, codewords in the initialization codebook are selected to construct background codebook based on the assumptions about the background model. The image can be partitioned to foreground and background according to the background codebook, and in the meanwhile codebook is updated correspondingly. Experimental results demonstrate the effectiveness of the method presented in this paper, even when there are moving clutters in the background and illumination variations.