A moving object detection method based on sliding window Gaussian mixture model

In complex scenes, the traditional Gaussian Mixture Model (GMM) algorithm is an effective way to extract moving objects. However, after a period of modeling, the model's updating speed is difficult to keep up with the changes of true background. Consequently, false alarm rate will be increased in moving object detection. This paper proposed a new moving object detection method, which utilizes sliding window technology to retain short-term historical information. To a certain extent, it remedies that the traditional mixture Gaussian background model can not form new background in time, and it improves the integrality of motion detection. Furthermore, it reduces the algorithm sensitivity for the scene illumination change. The experimental results show that the proposed algorithm can more accurately, perfectly detect moving targets, and adapts rapidly to variations in the environment.