Comparative Analysis of Calibration Methods for a Static Camera

In computer vision, camera calibration is a procedure that tries to know how a camera projects a 3D object on the screen. This process is necessary in those applications where metric information of the environment must be derived from images. Many methods have been developed in the last few years to calibrate cameras, but very few works (i.e. Tsai [10], Salvi and Armangué [8], Lai [7] or Isern [5]) have been done to compare such methods or to provide the user with hints on the suitability of certain algorithms under particular circumstances. This work presents a comparative analysis of eight calibration methods for static cameras using a pattern as reference: Faugeras [4], Tsai [9] (classic and optimized version), Lineal, Ahmed [1] and Heikkilä [6] methods, which use a single view of a non-planar pattern; Batista’s method [3] which uses a single view of a planar pattern; and Zhang’s method [11] which uses multiple views of a planar pattern. The results provided by every method have been compared and the accurac