针对子块级背景建模方法无法保证所提取前景形状的精确性及像素级背景建模方法无法有效处理非平稳场景的问题，提出了一种背景建模分层模型，首先采用文中子块级建模算法得到较为粗糙的背景区域和前景区域，然后利用混合高斯模型对特定图像区域执行像素级的前景提纯或背景模型更新操作，2 种不同层次的算法通过非

对称前向反馈机制进行级联。实验结果表明，所提分层模型在能够有效处理非平稳场景的同时保证了所提取前景

形状的精确性，且对光照突变不敏感，建模效果优于级联算法中任一独立算法，而处理时间小于2 种独立算法处理时间之和，满足了实时处理要求-Block-based background modeling couldn’t obtain the exact shape of foreground, while pixel-based approaches

couldn’t handle non-stationary backgrounds effectively. To solve the problem, a hierarchical scheme for background

modeling was presented. The hierarchical model used block-based method proposed to obtain coarse background and foreground

regions firstly, and then the operations of pixel-level foreground refining and model updating based on Gaussian

mixture model were performed on special regions of the input image. These two algorithms in different levels were combined

by adopting an asymmetric feed-forward strategy. Experimental results show that the hierarchical method proposed

can obtain the exact shape of foreground and process non-stationary scenes well, in addition, it is insensitive to illumination

change and can provide better results than any single approach in it, meanwhile, the integrated computation time is

shorter than the sum of those of running the block an