Reducing the Foreground Aperture Problem in Mixture of Gaussians Based Motion Detection
Ákos Utasi, László Czúni · 2007
Separating the moving image parts from the static background is an important phase in video surveillance applications. The method based on mixture of Gaussians (MOG) is an often used and robust approach to learn the background automatically and adaptively. Known MOG methods often suffer from the phenomena called the foreground aperture problem, when parts of large moving homogenous regions become part of the background instead of being selected as moving pixels. This article introduces a new method to eliminate this problem.