An automatic ego motion compensation based point correspondence algorithm is presented. A basic problem in autonomous navigation and motion estimation is automatically detecting and tracking features over consecutive frames, a challenging problem when the camera motion is significant. In general, feature displacement over consecutive frames can be approximately decomposed into two components: (i) the displacement due to camera motion which can be compensated by image rotation, scaling, and translation; (ii) the displacement due to object motion and/or perspective projection. In this paper, we introduce a two step approach: First, the motion of the camera is estimated using a computational vision based image registration algorithm. Then consecutive frames are transformed to the same coordinate system and the feature correspondence problem is solved as one of tracking moving objects using a still camera. Methods for subpixel accuracy feature matching and tracking are introduced. The approach results in a robust and efficient algorithm. Results on several real image sequences are presented.