西南交通大学学报2012，Vol.47Issue(5)：769-775,7.DOI:10.3969/j.issn.0258-2724.2012.05.007
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
摘要
Abstract
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.关键词
背景差/加权核密度估计/自适应阈值Key words
background subtraction/ weighted kernel density estimation/ adaptive threshold分类
信息技术与安全科学引用本文复制引用
蒋鹏,金炜东..基于加权核密度估计的自适应运动前景检测方法[J].西南交通大学学报,2012,47(5):769-775,7.基金项目
国家自然科学基金资助项目(61134002):中央高校基本科研业务费专项资金资助项目(SWJTU12CX027) （61134002）
