Moving objects detection method based on a fast convergence Gaussian mixture model

Background Modeling is the normal method on the moving objects detection, it plays a key role in the moving objects detection and tracking, the Gaussian mixture model is one of the most successful methods on the detection. But it converges slowly in the complex scene. This paper proposes a new method named “additive increase” and the “additive decrease” to adjust the weight of the matched distributions and the unmatched distributions respectively. The method can speed up the mixture model convergence process. In order to reduce the noise, “noise restraint base on the adjacent region” approach is using to increase the probability of classifying each pixel correctly during the moving objects detection.