Comparing Self-Calibration Methods for Static Cameras

Many methods have been developed in the last few years to self-calibrate cameras, but few works have addressed a comparison of such methods to provide the user with hints on the suitability of certain algorithms under particular circumstances. The few of these works that analyze self-calibration methods have concentrated on the study of the influence of some factors [1] [2] or the identification of critical movements [3], but there are not any studies analyzing the accuracy and stability of these methods. This work presents a comparative analysis of four methods of self-calibration for cameras which only rotate (without translation movement): McLauchlan’s method [4] that assumes that the camera setup is fixed along the sequence; and Agapito’s methods (linear [5] and iterative [6] algorithms) and Seo’s method [7] that allow the variation of optic center and scale factors. The experiments discussed in this paper have focused on characterizing the accuracy in the point reconstruction (global error) and the stability and accurac