Dynamic Video Segmentation via a Novel Recursive Kernel Density Estimation

Abstract

Modeling background and segmenting moving objects are significant techniques for video segmentation and other video processing applications. Many different methods about background modeling and video extraction have been proposed over the recent years. In this paper, we present a novel recursive Kernel Density Estimation based video segmentation method. In the algorithm, local maximum in the density functions is approximated recursively via a mean shift method firstly. Via a proposed thresholding scheme, components and parameters in the mixture Gaussian distributions can be selected adaptively, and finally converge to a relative stable background distribution mode. In the segmentation, foreground is firstly separated by simple background subtraction method. And then, the Bayes classifier is introduced to eliminate the misclassifications points to improve the segmentation quality. Experiments on four typical video clips are used to compare with some previous methods.