Image Object Detection Algorithm Based on Improved Gaussian Mixture Model

Aiming at poor adaptability to illumination variation and single learning rate in traditional Gaussian mixture model, an improved moving object detection algorithm based on adaptive Gaussian mixture model is proposed in this paper, so as to achieve the goal of a self-adaptive background updating model. In this paper, we analyze the existed algorithms and put forward the method to make use of color histogram matching algorithm, through introduction of illumination variation factor and update-counter of model parameter and the components number with self-adaptive selection employed to adaptively adjust learning rate, in order to greatly reduce the computation time of the algorithm and improve the real-time performance. The experiment results show that the new method can effectively adapt the scene, and has more good expansibility, robustness and stability than traditional Gaussian mixture model.