Determining the status of a runway prior to landing is essential for any aircraft, whether manned or unmanned. In this paper, we present a method that can detect moving objects on the runway from an onboard infrared camera prior to the landing phase. Since the runway is a planar surface, we first locally stabilize the sequence to automatically selected reference frames using feature points in the neighborhood of the runway. Next, we normalize the stabilized sequence to compensate for the global intensity variation caused by the gain control of the infrared camera. We then create a background model to learn an appearance model of the runway. Finally, we identify moving objects by comparing the image sequence with the background model. We have tested our system with both synthetic and real world data and show that it can detect distant moving objects on the runway. We also provide a quantitative analysis of the performance with respect to variations in size, direction and speed of the target.