This paper is concerned with the generating an efficient fundamental matrix for image rectification problem under motion. Computation of fundamental matrix is the information source to estimate the accurate optic flow in computer vision area. This includes extraction of images from real world, using 8-point algorithm, and calibration for predicting optic flow. We are performing a comparative study over different fundamental matrix, and implementation techniques to propose a method which reduces the complexity in analysis of an image rectification and improves computational speed for vehicle head lamp tracking. Fundamental matrix estimation is difficult since it is often based correspondences that are spoilt by noise and outliers. Outliers must be thrown out via robust statistics, and noise gives uncertainty. In this article we provide a closed-form formula for the uncertainty of the so-called 8 point algorithm, which is a basic tool for fundamental matrix estimation via robust methods. As an application, we modify a well established robust algorithm accordingly, leading to a new criterion to recover point correspondences under epipolar constraint, balanced by the uncertainty of the estimation.