Moving object detection algorithm based on Gaussian mixture model
Zhao Wei Qu · Applied science and technology · 2015
Under the static condition of a camera for adaptive moving target detection,this paper puts forward an improved algorithm for moving object detection. First of all,considering that in the early stage of Gaussian mixture background modeling,the background modeling effect is not ideal,the background model is obtained by statistical method at the beginning of the video sequence,and then Gaussian mixture models are set up for the background image; then,in aspect of the model learning,different rates of learning are set for the mean and variance in order to improve the convergence rate of the background model. In view of the defects of the traditional LBP operator,an improved texture feature operator is proposed. This improved operator is combined with the method of removing shadow area of the HSV color space,thereby to detect and get rid of the shadow,and further to achieve detection of the edge of human head according to the principle of random Hough operator's detection of the ring. The experimental results show that the proposed algorithm can well detect moving targets,and can effectively remove the shadow in the moving object and thereby to achieve the detection of head area.