Nowadays image motion analysis is considered as a significant subject in image processing and according to its characteristics is divided into different categories. This paper focuses on camera jitter as fast translations in image sequence due to an unstable platform. Our studies have led to do correlation matching [Motion Displacement Estimation Using an Affine Model for Image Matching] between well featured templates[Edge Detection using KL Transform, Extracting Good Features for Motion Estimation] of successive frames to prevent aperture problems and thus have good estimation of the translations. But camera vibration may also suffer from rotation and scaling as nonlinear motions [Detection of Non-Uniform Motion in Image Sequences using a Reduced Order Likelihood Ratio Test] that may defect the matching process. Therefore mapping of 3D image points into camera image plane has been investigated to trace the effects of these nonlinear parameters in 2D motion equations [Image Based Measurement Systems]. Now by approximating these equations for minute rotation and bounded scaling between frame i and i+1, the set of motion equations with four unknowns (rotation, scaling, horizontal /vertical translations) could be solved just by LSE [Kalman Filtering, Theory and Practice using MATLAB] method for a unique and optimized response. After all when you apply the estimated displacements on each frame for compensation, an stabilized sequence will conclude.