Gaussian mixture model moving object detection based on foreground of edge images
Guo We · Computer Engineering and Applications Journal · 2015
Gaussian mixture model has been widely used for background modeling. However, the detection result is easily affected by noise and illumination mutation. In order to solve this problem, this paper proposes to combine the improved Gaussian mixture model with edge information. Once the method of three frame difference detects changes of the environment, the learning rate will be adjusted adaptively. The improved Gaussian mixture model is applied to extracting edge images and foreground images of moving objects. After dilating edge images, the result is obtained by computing the intersection of edge images and foreground images, and filling the hollow part based on the information of optical flow. Experimental results indicate that the proposed method has great capacity in restraining noise and dealing with illumination mutation,and improves the performance of object detection. It is more efficient than the traditional method.