An Effective Method for Foreground Segmentation of Video
In this paper, we propose a novel foreground segmentation approach for applications using static cameras. The foreground segmentation is modeled as an energy function optimum process, where energy function is based on Markov Random Field (MRF) and efficiently optimized by Gibbs sampling. The essence of our method is that we fuse four foreground/background models based on color and texture. This allows composing a robust likelihood term that not only reflects the appearance of foreground/background, but also models the shadow removal process, together with a spatial contrast term and a better temporal persistence term, which achieves a more accurate segmentation. This method has been run on both indoor and outdoor sequences, and the results have proved its effectiveness.
foreground segmentation MRF shadow removal
Jianfeng Shen Zongqing Lu Qingmin Liao
Department of Electronic Engineering, Tsinghua University Graduate School at Shenzhen, Tsinghua University Shenzhen, P.R.China 518055
国际会议
The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)
西安
英文
288-292
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）