基于风险决策混合高斯背景建模快速检测前景目标突变

将风险决策引入前景目标的突变判断中，通过设计一个时序计数器函数来记录图像上某一像素点被划为前景的次数，当次数大于某一阈值时，将该像素从前景点改判为背景点，据此可以估计该像素点为背景点的概率，做出风险决策，以便及时更新混合高斯背景模型参数，从而减少多个高斯模型的高额计算量．最后通过实验验证了算法在目标检测率和实时性方面的改进．

Rapid Detection of Foreground Target Mutations Based on Risk Decision of Gaussian Mixture Background Model

The adaptive Gaussian Mixture Background Model is often used to detect moving target with fixed camera. In this paper， risk decision is introduced to judge mutations of foreground target. A timing counter function is designed to record the times of a pixel judged as foreground pixel. When the times is greater than a certain threshold， the pixel will be judged as a background pixel instead of a foreground pixel. So the probability of background pixel can be evaluated for making risk decision， updating the GMM parameters and reducing computation of the multiple Gaussian model. Finally， the algorithm is verified by experiments in the target detection rate and real-time improvements.