会议专题

Video Background Subtracion Using Improved Adaptive-K Gaussian Mixture Model

Video stream segmentation is a critical step in many computer vision applications. Background subtraction based on Gaussian Mixture Model (GMM) is a commonly used technique for video segmentation. In this paper, an improved Adaptive-K Gaussian Mixture Model (AKGMM) method was presented for updating background. The dimension of the parameter space at each pixel can be adjusted adaptively according to the frequency of pixel value changes. The number of GMM reflected the complexity of pattern at the pixel. Experimental results demonstrated that the proposed method is more adaptive and robust than some existing approaches.

Adaptive-K Gaussian Mixture Model Background Subtraction

Hao Zhou Xuejie Zhang Yun Gao Pengfei Yu

Information School of Yunnan UniversityKunming China Information School of Yunnan University Kunming China

国际会议

2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

成都

英文

1-4

2010-08-20(万方平台首次上网日期,不代表论文的发表时间)