Background segmentation of dynamic scenes based on dual model

Detecting moving objects from background in video sequences is the first step of many image applications. The background can be divided into two types according to whether the pixel values of it are variable or not: static one and dynamic one. How to correctly detect moving foreground objects from dynamic scenes is a difficult problem because of the similarity between the moving foreground and the variable background. In this study, a new method for non-parametric background segmentation of dynamic scenes is proposed. Here the background is described by two interrelated models. One of them is called the self-model, which concerns with the recently observed pixel values at the same position, and the other one is called the neighbourhood-model, which is described by the pixel values of the neighbourhood. The author´s method can accurately detect the dynamic background. To correctly detect pixels in the foreground as much as possible, the authors also propose an adaptive threshold for foreground decision based on the background characteristics. All of the above detection processes can be done in real time. Experimental results on public dataset demonstrate that the proposed method outperforms the state-of-the-art for background segmentation in dynamic scenes.