Detecting moving objects is an important part of tracking. Most of the previous work on moving object detection concentrates on fixed cameras. Methods using moving cameras seldom deal with the problem of robustly and continuously updating the background model during all times including the periods when the camera is not static. We propose a method to build and continuously update a background model, and to detect foreground objects not only when the camera is static but also when it is zooming in/out or panning/tilting. For instance, the model built for the zoomed in (out) portion of a video is warped to the reference frame of the model of the zoomed out (in) portion to immediately incorporate changes that occurred in the background, such as objects that are placed or removed. This way, changes are incorporated to the model without requiring a learning period each time camera zooms in/out. This method addresses the problems of detecting moving objects during the zooming in and zooming out periods, detecting objects that are placed in the scene while the camera is non-static and gradually incorporating an overall illumination change to the scene model. We present different experiments covering three different scenarios to demonstrate the success of the proposed method in addressing these issues.