复杂场景下的运动前景提取是计算机视觉研究领域的研究重点。为解决复杂场景中的前景目标提取问题，提出一种应用于复杂变化场景中的基于混合高斯模型的自适应前景提取方法。该方法可以对视频帧中每个像素的高斯分布数进行动态控制，并且通过在线期望最大化（EM）算法对高斯分布的各参数进行学习，此外每个像素的权值更新速率可根据策略进行调整。实验结果表明，该方法对复杂变化场景具有较好的适应性，可有效、快速地提取前景目标，提取结果具有较好的查准率和查全率。

Foreground detection is a significant step of information acquisition in intelligent surveillance. The task is to segment all the moving objects from complex scenes without any false targets and noise interference. This step is a premise of the following steps: object identification, object tracking and behavioral analysis. Due to non-stationary surveillance scenes, foreground extraction becomes a complex task with many challenges. The performance of foreground detection mainly depends on the background modeling algorithm. In order to solve this problem, an adaptive background modeling approach is proposed. This approach is based on a Gaussian mixture model proposed by Stauffer and Grimson. In their approach, each pixel maintains a Gaussian mixture model constituted by K Gaussians. Then each Gaussian mixture model is updated by new observe pixel value. However the strategies of updating have some limits, such as fixed Gaussian number, fixed parameters, and fixed learning rate. The proposed approach optimizes updating strategies so as to break these limits. In this approach, each pixel maintains a dynamic Gaussian mixture model, while the number of Gaussians can be controlled dynamically. Further more, an online EM algorithm is applied to the method for estimating the parameters in Gaussian mixture model. At last, several strategies are proposed to control the learning rate of weights. Experimental results show that the foreground object detection approach has good adaptability to complex environments. The foreground object can be detected effectively and rapidly, and the precision and recall ratio of results demonstrate superiority of the method to some related work.