Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (6): 170-174.

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Foreground object detection based on background subtraction image kernel density estimation

LIU Di, GAO Meifeng   

  1. Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Online:2013-03-15 Published:2013-03-14

基于背景差分的核密度估计前景检测方法

刘  娣,高美凤   

  1. 江南大学 轻工过程先进控制教育部重点实验室,江苏 无锡 214122

Abstract: In view of existing situation that foreground object detection is imprecise and calculation is large in non-parametric kernel density estimation, the foreground object detection algorithm based on background subtraction image kernel density estimation is proposed. The single-Gaussian model and the non-parametric kernel density estimation are applied to building the initialization scene background. By means of background subtraction image, the non-dynamic background region is filtered. Kernel density estimation is used to estimate the motion object. For the dynamic background region, the algorithm uses non-parametric kernel density estimation algorithm to update it, otherwise, the percent of background and current frame is used to progressively update the non-dynamic background region. The experimental results show that this algorithm can separate foreground object accurately, greatly eliminate the false detection and reduce the calculated amount.

Key words: background subtraction, kernel density estimation, foreground object detection, background updating

摘要: 针对非参数核密度估计算法前景检测不够精确、运算量大的问题,提出了一种基于背景差分图像的核密度估计前景检测方法。该方法结合了单高斯模型和核密度估计模型进行初始背景建模,利用背景差分图像,过滤掉非动态背景区域,对动态背景区域采用核密度估计进行像素分类。同时,对非动态背景区域,采用渐进式更新;对动态背景区域,采用非参数核密度估计进行更新。实验结果表明,该算法能够精确地分割出前景目标,减少了误检噪声,降低了运算量。

关键词: 背景差分, 核密度估计, 前景目标检测, 背景更新