Method of Quick Background Updating Based on Gaussian Mixture Models
Xu We · Video Engineering · 2014
The traditional mixture Gaussian models can not respond promptly to sudden changes in the background,and the smear phenomenon is inevitable in the RunningAvg update algorithm. Therefore,an approach for quick background updating method is proposed. Firstly,two backgrounds are constructed separately based on the Gaussian mixture models and the RunningAvg update algorithm. Then the binarized differential image DB of the two backgrounds is obtained to get the regions of variation in the scene. Logicalandoperation is utilized between the binary image DB and the binary foreground image FB in order to extract the regions of variation accurately,which can eliminate the negative impacts resulted from the smear phenomenon. Afterwards,the information of the regions of variation,which is saved in the state table,is used to update the background models selectively depending on the changes in the regions of variation. As a result,the updating time of the regions of variation in the background models is reduced. The results have shown that the proposed method can not only strongly adapt to the sudden changes of the scene,but also can efficiently avoid the interference caused by the smear phenomenon and the short-term stay of the objects.