The interested objects or events is often related to moving objects in the scene, so moving object detection become a key step and challenging problem. This paper studies the detection of moving objects with a moving camera, and the homography transform is used as the background motion model. Only the background motion is involved in the estimation of the model without the interference of the foreground motion during the estimation of the model parameters. A dualmode singleGaussian model is adopted to prevent the background model from being contaminated by foreground pixels and the background model is transferred in continuous frames using the motionfusion based compensation method. A foreground probability map is also erected according to the temporal and spatial attributes of the foreground object and is intended for an implementation of the adaptive decision threshold constructed to the selected pixels. Compared with other algorithms on a unified video sequence, the experimental results show that the proposed algorithm has a good performance on detection of which is proved to be a realtime approach, and the precision, the recall and the Fmeasure value increased by 052%, 3337% and 2014% respectively.