An Optimization Algorithm of Foreground Objects Extraction

Abstract

In this paper, we propose a novel algorithm for coarse-to-fine foreground objects extraction. There are two general approaches for foreground objects extraction: background subtraction and image matting. Our new approach can not only improve detection accuracy compared with general background subtraction approaches, but also reduce computation burden compared with general image matting approaches. Firstly, we present a novel method called Motion-mask Gaussian Mixture Models (Motion-mask GMMs) to extract coarse foreground regions. This new approach can classify foreground and background pixels more accurately, especially when there are long-time stopping objects in the scene. Secondly, with the coarse foreground regions, we propose a novel approach to make foreground object extraction more accurate based on effective fusion of image registration and image matting. This new method overcomes the template drift problem during template updating and also reduces the expensive computational cost of image matting. Our proposed approach is tested with kinds of video sequences in indoor and outdoor environments. Experimental results demonstrate the accuracy and efficiency of our proposed approach for foreground object extraction.