Foreground object detection based on time information window adaptive kernel density estimation
Quan Liu · Journal of Communications · 2011
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 win-dow-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 back-ground 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 de-tection.Moreover,the algorithm also greatly improved the speed of the detection processing.