摘要
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）.
出处
《电子与信息学报》
EI
CSCD
北大核心
2012年第2期388-392,共5页
Journal of Electronics & Information Technology
基金
国家973计划项目(2007CB311100)
广东省高等学校高层次人才项目(201079)
广州市科技计划(11C42080722)资助课题
关键词
目标检测
复杂背景
贝叶斯-全概率联合估计
误差控制变量
Target detection
Complex background
Bayes-total probability joint estimation
Error control variable
