快速无参数背景建模的红外目标检测方法研究

针对复杂背景红外图像序列目标检测的难题,给出了一种用于红外监控系统中入侵目标检测的背景建模方法。应用特征样本集为每一个像素建立统计无参数样本集模型,根据核函数估计计算每一个像素值对模型的符合概率。使用双阈值进行目标检测和模型更新,将图像分为三类:可靠背景、感兴趣区域和不可靠背景。通过不可靠背景类提供的信息进一步将感兴趣区域细分为入侵目标和错误检测。对几种红外图像序列仿真实验表明,该算法不仅可以比较精确的检测显著入侵目标,对于容易淹没在噪声中的弱小入侵目标也可以实现准确地检测。

Object detection in infrared image with fast nonparametric background model

A background subtraction method is introduced with nonparametric background model for infrared surveillance application.This model employs a sample set as the statistical model of each pixel,and calculates conforming possibility of a pixel's value with kernel estimation.Two thresholds are adopted for object detection and model updating,which segments the frame into three categories:reliable background,unreliable background and interest region.Interest region is segmented into intruding object and false positive detection with context provided by unreliable background.Experiments with several infrared image sequences show that this method could precisely detect salient intruding object and weak intruding object that is easy to be confused with noise.