An Optimization Algorithm of Foreground Objects Extraction
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.
Gaussian Misture Model image matting image registration object extraction
Xiayi Zhang Zhipeng Li Fuqiang Liu Zhen Jia Jianwei Zhao
Department of Information and Communication Engineering, Tongji University Shanghai, China United Technologies Research Center Shanghai, China
国际会议
2010 6th International Conference on MENS NANO,and Smart System(2010年微机电纳米、智能系统国际会议 ICMENS 2010)
长沙
英文
155-159
2010-12-14（万方平台首次上网日期，不代表论文的发表时间）