An adaptively weighted algorithm for camera calibration with 1D objects

Abstract

Camera calibration is a necessary step to extract 3D metric information from 2D images. 1D object-based camera calibration has recently received attention because there is no self-occlusion problem for a 1D object. In this paper, an adaptively weighted 1D calibration algorithm with high accuracy is presented. The innovation and contributions of such an algorithm are two-fold: one is that data normalization is used to improve estimation accuracy of the relative depths of marker points on the 1D calibration object; the other one is that a weighted coefficient is adaptively assigned to each constraint on the camera parameters by analyzing the data error involved in the constraint. Experiments with synthetic and real image data show that the accuracy of the proposed algorithm is much higher than that of classical 1D calibration. In some cases, it outperforms the nonlinear optimal 1D algorithm and is even comparable with the normalized 2D and 3D calibration.