在很多计算机视觉应用中，一个基础而关键的任务是从视频序列中确定运动目标，其中对于固定摄像机的监控视频运动目标的检测，最常用的方法是减背景技术。其思想是将视频帧与一个背景模型做比较，其中区别较大的像素区域被认为是运动目标。但由于构建背景模型需要考虑光照变化等很多因素，因此开发一个好的减背景算法面临很多挑战。为了使人们对该技术有个初步了解，该文首先对利用减背景技术实现运动目标检测的过程、目前各种典型背景建模算法的原理和优缺点做了较为详细的阐述和归纳，然后总结了各种减背景算法的总体特点，并结合实验和文献资料对部分算法进行了对比评价，最后指出了减背景技术的未来研究重点和发展方向。

Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. For surveillance video captured by static camera, the common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. However, there are many challenges in developing a good background subtraction algorithm for many factors such as changes in illumination should be considered in constructing a background model. This paper firstly expounds the universal process of detecting objects with background subtraction, all typical background modeling algorithms and their merits. Then their whole characteristics are summarized and the performances of some algorithms are compared based on experiments and other literatures. Finally, key issues and directions of future study in this area are pointed out.