This paper describes a system that estimates the 3D motion of the camera in a cluttered scene containing moving objects from a stereo image sequence. We use KLT trackers to detect and track landmarks in both camera sequences. Range information gives an approximation of the depth for the landmarks and helps us to build a 3D system equation for the scene. By taking a novel method to detect outliers in landmark set from depth discontinuities, the filtered landmarks are then run through an iterated weighted linear square method
with a retiring scheme. The estimated ego motion helps warp images of the scene, which enables us to find foreground objects from stabilized images. We describe the overall system as well as the details of the stabilization along with images that show the results of the stabilization results.