Video Segmentation Into Background and Foreground Using Simplified Mean Shift Filter And Clustering

Abstract

Video Segmentation decomposes image frames into background and foreground. In this paper, a combination of simplified mean-shift filter and K-Means clustering are used in modeling the background. The most common models used for background estimation are mixture of Gaussian (MOG), Kernel Density Estimation (KDE), etc. Comparison of the proposed approach with some of the aforementioned models have been made and it was observed that a relatively simple model using a simplified mean-shift computation and K-Means clustering can produce results that are comparable to those obtained by other methods. The proposed approach was tested on video data obtained from Wallflower test images from its source website. The results are encouraging and show the validity of this approach for background modeling.