Camera Motion Parameter Estimation Technique Using 2D Homography and LM Method Based on Projective and Permutation Invariant Features

Precise camera calibration is a core requirement of location system and augmented reality. In this paper, we propose a method to estimate camera motion parameter based on invariant point features. Typically, feature information of image has drawback, it is variable to camera viewpoint, and therefore information quantity increases after time. The LM (Levenberg-Marquardt) method using nonlinear minimum square evaluation also has a weak point, which has different iteration number for approaching the minimal point according to the initial values and convergence time increases if the process run into a local minimum. In order to complement these shortfalls, we, first propose constructing invariant features of geometry using similarity function and Graham search method. Secondly, we propose a two-stage camera parameter calculation method to improve accuracy and convergence by using 2D homography and LM method. In the experiment, we compare and analyze the proposed method with existing method to demonstrate the superiority of the proposed algorithms.