Research on Algorithm of Video Segmentation Based on Self-Adaptive Single Gaussian Model

Abstract

Single Gaussian background modeling is often used in the static scene. In order to overcome the deficiency of the traditional single Gaussian background subtraction method, an improved adaptive background modeling algorithm was proposed. First, the number of each pixel in the image determined as foreground that were as the parameters of each pixel update rate were counted respectively. So the original fixed update rate was transformed into the dynamic update rate changing with the parameter. In this way, the phenomena of ghosting and shelling was suppressed effectively in the traditional single model of Gauss. Finally, foreground objects were obtained by using the method of shadow removal based on morphological reconstruction. The experimental results indicate that the algorithm can quickly and accurately establish and update the background model, and extract the moving object effectively. Compared with existing approaches, experimental results with different real scenes demonstrate the robustness of the proposed method.