Improved object detection method of adaptive Gaussian mixture model

Abstract

The deficiency of Gaussian Mixture Model( GMM) is of high computation cost and cannot deal with the shadow and ghosting. An improved foreground detection algorithm based on GMM was proposed in this paper. By analyzing the stability of the background, intermittent or continuous frame updating was chosen to update the parameters of the GMM. It can efficiently reduce the runtime of the algorithm. In the background updating, the updating rate was associated with the weight and this made it change with the weight. The background pixels which appeared after the objects moving were set a larger updating rate. It can improve the stability of the background and solve the problem of ghosting phenomenon and the transformation of background and foreground. After objects detection, the algorithm eliminated the shadow based on the RGB color space distortion model and treated the result by Gauss pyramid filtering and morphological filtering. Through the whole process, a better contour was obtained. The experimental results show that this algorithm improves the calculation efficiency and accurately segments the foreground object.