An improved background and foreground modeling using kernel density estimation in moving object detection

Abstract

For the purpose of precisely distinguishing the true moving target and the background in video surveillance, many strategies based on both background and foreground modeling have been proposed recent years. In this paper, we presented an improved moving object detection algorithm based on kernel density estimation which has two features. First, we construct a novel background and foreground model based on the basic nonparametric kernel density estimation and a joint domain-range foreground model. The foreground model applied here assures a more accurate detection result especially with dynamic backgrounds and building background with basic kernel density estimation helps to reduce the amount of computational cost which is usually a large number in many of the exists background-foreground models. Second, we present a strategy using edge detection to adaptively updating the background. By taking this method, our algorithm carrying out a quite exactly detecting result while immediately adjust to the changes in the background model, such like illumination change, objects from movement to static or conversely. Experimental results show that our proposal efficiently suppressed the inaccuracy caused by multiple reasons.