Step-by-step video camera self-calibration method

Abstract

The invention provides a step-by-step video camera self-calibration method, which comprises the following steps: in the first phase, shooting two images of the same scene at different focal lengths, performing feature extraction and matching on the images and eliminating mismatching points, and solving principal point coordinates of the video camera by using matching point pairs; and in the second phase, shooting the same scene from different angles to obtain three images available for matching, and performing feature extraction and matching on the images and eliminating mismatching points, and then based on Kruppa equation, substituting the obtained principal point coordinates into the equation to accomplish solving the three parameters of an obliquity factor, as well as scale factors of the video camera in the directions U and V axes of an imaging plane. By using the method provided by the invention, the calibration accuracy of the principal point coordinates of the video camera is relatively high, and the coefficient matrix size of the Kruppa equation is reduced, the amount of solving computation is reduced, and the method has the characteristic of being real-time. The method is applicable to video camera calibration of a vision system and can be used in the fields of three-dimensional measurement, three-dimensional reconstruction, machine navigation, augment reality and the like.

Description

technical field [0001] The invention relates to the field of computer vision, more specifically, to a camera self-calibration method. Background technique [0002] With the continuous development of information and communication technology, 3D reconstruction and augmented reality technology in computer vision have been more and more widely used. Because the computer acquires two-dimensional image and graphic information, which lacks the necessary sense of reality, 3D reconstruction technology can restore the 3D structure of the scene by using two or more images of the scene or a video sequence, so that people can more comprehensively Augmented reality is to accurately place virtual objects in the real environment through computer technology, and realize the seamless integration of the virtual world and the real world with the help of various display devices, thus presenting a visual experience to users. New environments with realistic effects. 3D reconstruction and augment...

AI Technical Summary

Problems solved by technology

Most of the existing self-calibration methods use optimization methods to solve the Kruppa equation. The solution of the Kruppa equation is still a nonlinear problem, and the calibration results are easily disturbed by image noise. The calibration process takes a long time, and the robustness of the calibration results susceptible to noise

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