A robust least squares motion detection algorithm was evaluated with respect to target size, contrast and sensor noise. In addition, the importance of robust motion estimation was also investigated. The test sequences used for the evaluation were generated synthetically to simulate a forward looking airborne sensor moving with translation parallel to a flat background scene with an inserted target moving orthogonal to the camera motion. For each evaluation parameter, test sequences were generated and from the processed imagery the algorithm performance measured by calculating a receiver-operating-characteristic curve. Analysis of the results revealed that the presence of small amounts of noise results in poor performance. Other conclusions are that the algorithm performs extremely well following noise reduction, and that target contrast has little effect on performance. The system was also tested on several real sequences for which excellent segmentation was obtained. Finally, it was found that for small targets and a downward looking sensor, the performance of the basic least squares was only slightly inferior to the robust version. For larger targets and a forward looking sensor the robust version performed significantly better.