A recursive kernel density learning framework for robust foreground object segmentation

Dynamic video segmentation is an important research topic in computer vision. In this paper, we present a novel recursive Kernel Density Learning framework 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 a series of typical video clips are used to compare with some previous algorithms.