Recursive Kernel Density Estimation for modeling the background and segmenting moving objects

Abstract

Identifying moving objects in a video sequence is a fundamental and critical task in video surveillance, traffic monitoring, and gesture recognition in human-machine interface. In this paper, we present a novel recursive Kernel Density Estimation based background modeling method. First, local maximum in the density functions is recursively approximated using a mean shift method. Second, components and parameters in the mixture Gaussian distributions can be selected adaptively via a proposed thresholding mechanism, and finally converge to a stable background distribution model. In the scene segmentation, foreground is firstly separated by simple background subtraction approach. And then a local texture correlation operator is introduced to fill the vacancies and remove the fractional false foreground regions so as to obtain a better video segmentation quality. Experiments conducted on synthetic and video data demonstrate the superior performance of the proposed algorithms.