In order to build a robust background model and improve the accuracy of detection of foreground objects, the temporal correlation of pixels at the same position of the video image and the spatial correlation of neighboring pixels are considered comprehensively. This paper proposed a background modeling method based on multi-feature fusion. By using the domain correlation of pixels in a single frame image to quickly establish an initial background model whichis updated using pixel values, frequency, update time and sensitivity of the video image sequence, the ghost phenomenon is effectively improved and the holes and false prospects for moving targets are reduced. Through multiple sets of data tests, it shows that the algorithm improves the adaptability and robustness of dynamic background and complex background.