针对复杂背景下前景提取较为困难或者提取准确率较低等问题,该文提出了基于贝叶斯-全概率联合估计的目标检测模型并引入了背景误差控制变量的概念.通过选择适当的特征向量,在贝叶斯-全概率估计模型下,背景像素将会分为静止与运动两种不同的类型,进而准确提取前景像素点.实验结果表明,该模型是一个较为通用的目标检测模型,在目标提取时,该文算法对各种类型的视频背景环境(包括复杂背景)都具有较好的适用效果.%For the difficulty or low accuracy on foreground extraction in a complex environment, this paper proposes Bayes-total probability joint estimation for the detection and segmentation of foreground objects and the definition of background error control variable. Under the criterion of Bayes-total probability joint estimation, background pixels will be divided into stationary and moving types by choosing a proper feature vector, and foreground pixels can be detected accurately. Experiment results show the proposed method is a more general model for target detection, and it is also promising in extracting foreground objects under different kinds of background from video (containing complex background).
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