：基于背景建模的运动目标分割是智能视频监控的重要任务，模型的质量直接影响到检测、跟踪、识别等运动分析的准确性．当前的建模方法多是单层的，忽略了像素特征在时域和空域上的联系，模型描述不够准确，对于背景扰动、全局光照变化及复杂的室内外场景等多种情况鲁棒性不强，导致了分割中出现空洞和噪声点．针对这些问题提出了一种双层建模的方法，在第一层提取时域上的像素亮度特征采用码本建模，第二层提取邻域纹理特征采用基于中心对称的局部二值模式建模．实验证明该方法在用于运动分割时，比常用方法具有更好的准确性和鲁棒性．-Moving object segmentation based on background modeling is an important task of intelligent video surveillance, quality models directly affects the detection, tracking, recognition accuracy motion analysis. The current modeling methods are mostly single, ignoring the characteristics of pixels on the temporal and spatial linkages, model descr iption is not accurate enough, the background disturbances, global illumination changes and complex indoor and outdoor scenes and other conditions are not robust strong, leading to a hollow-point division and noise appear. To solve these problems presents a double-modeling method, the pixel brightness feature extraction using time domain modeling codebook in the first layer, the second layer of texture feature extraction using neighborhood center symmetric local binary pattern based construction mode. Experiments show that the method used for motion segmentation when compared with the conventional method has better accuracy and robustness.