基于加权核密度估计的自适应运动前景检测方法

Adaptive Foreground Detection Based on Weighted Kernel Density Estimation

为解决监控视频背景初始化过程中前景干扰的问题,提出了一种基于加权核密度估计(KDE)的自适应运动前景检测方法.该方法对时间域变化稳定的像素值进行加权,并利用核密度估计构建背景模型,避免了背景初始化过程中前景的干扰.基于该背景模型,提出了一种新的阈值设定策略.该策略根据前景空间分布的连续性自适应获得前景阈值,填充前景中的"孔",并更新阈值.实验结果表明:即使场景中存在运动前景,该方法能够在多种场景下获得90%以上的查准率和查全率,其性能优于传统的背景差法.

In order to avoid the impacts of moving foreground on background modeling in training stage,an adaptive foreground detection method based on weighted kernel density estimation（KDE） was proposed.In this method,temporal stable pixels are assigned more weights,and a weighted KDE background model is established to reduce the interference of foreground during background model building.Based on this background model,a strategy for dynamic foreground threshold was proposed.With the spatial consistency of foreground,＂holes＂ in foreground are filled and thresholds are updated in the same time.The experimental results show that the proposed foreground detection method is able to achieve over 90% precise and recall rates in various scenes even under the condition that there are moving objects,and it outperforms the conventional background subtraction methods.