For non-parametric kernel density estimation information redundancy and repetition computation in the training stage estimate error and large amount of calculation in the estimated phase

this paper proposed a method of clustering difference image kernel density estimation for foreground object detection.We first choose those samples that have higher frequency and diversity to contain important information based on max-min distance clustering in training sequence.A Gausisian KDE is built to estimatea motion object after adaptive threshold image difference calculation.Experimental results were given to demonstrate that the proposed algorithms are elimination of the typical non-movement noise point for estimated error and improving real-time capability.