To meet the real-time demand of moving object detection in Unmanned Air Vehicle (UAV), and to cope with the problems of moving background and variable illumination, a novel moving object detection technique based on Single Gaussian Model (SGM) and optical flow is presented. First, an improved SGM is applied to model the background of the image captured by moving camera, and then the corresponding models of previous frame are fused to compensate the motion of camera. Second, the obtained foreground is used as a mask to extract feature points to calculate optical flow, and then these sparse points are clustered to detect the objects. Experimental results demonstrated the effectiveness of the proposed approach in preventing the background model of SGM from being contaminated by the foreground, as well as dealing with illumination changes. It can also update background model quickly and obtain moving objects precisely.