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MOVING OBJECT DETECTION USING STATISTICAL BACKGROUND SUBTRACTION FOR A ROTATING CAMERA

In this paper, an algorithm is presented that models the background of a scene observed by a rotating camera. Here, adaptive Gaussian mixtures model is used to estimate the background model. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current frame is classified as belonging to the foreground or background. This method yields improved results in detecting moving objects and building background model in the presence of moving objects. A test case involving a surveillance system to detect moving objects, especially car as a object is demonstrated successfully. For developing our method, a real time video sequence acquired from a rotating camera is used. Our method can effectively deal with the scenes, such as the outdoor and the cluttered one. The experimental results show our method is quite accurate.