基于时间窗的自适应核密度估计运动检测方法

摘要

在对非参数核密度估计算法改进的幕础上,针对远程视频监控中存在前景检测不够精确、实时性低等问题,提出了用于自适应背景更新的基于像素时间信息窗的核密度估计(TIW-KDE)算法,该算法充分利用时间轴上的前景帧的信息,自适应地将背景划分为动态背景区域和非动态背景区域,对动态背景区域用改进的非参数核密度估计算法进行更新,对非动态背景区域采用渐进式算法更新,有效解决了非参数核密度估计算法在背景更新时引起的背景污染和计算量大问题.实验结果表明,该算法在提高前景检测精确性的前提下,在处理实时性方面得到很大提高.%There exist some problems, such as imprecise foreground object detection and lower real-time in remote video monitoring. Based modified non-parametric kernel density estimation, a new algorithm using time information window-kernel density estimation (TIW-KDE) was proposed for adaptive background updating. The algorithm, which took full advantage of the information on the foreground frames along the time line, divided the background into dynamic background region and non-dynamic background region. For the dynamic background region, the algorithm used non-parametric kernel density estimation algorithm to update it, otherwise, the percent of background and current frame was used to progressively update the non-dynamic background region. This effectively settled the problems of background dirt and decreases the complexity of computation in the background updating phase of the non-parametric kernel density estimation. The experimental results show that the algorithm improved the accuracy of the foreground object detection. Moreover, the algorithm also greatly improved the speed of the detection processing.