For real-time detection of moving object

the general and simple method is based on background image difference.However

it requires the accurate current background image

and so far

no reasonable approach has been designed and implemented for automatic background updating along with the illumination variance

which limits its applications.To overcome the above problem

a new self-adaptive background approximating and updating algorithm based on optical flow theory is first presented in this paper.Moreover

the difference image is obtained by using a color image difference model

and then a self-adaptive thresholding segmentation method for moving object detection based on Gauss model is developed and implemented.Experimental results demonstrate that the proposed new background updating method can update the background exactly and quickly along with the variance of illumination

and the self-adaptive thresholding segmentation method based on Gauss model can extract the moving object regions accurately and completely

which is the foundation for further objects recognition and understanding.