Abstract: It is well recognized that the IAC (Image of the Absolute Conic) based camera calibration techniques are not quite robust, however, there are few reports on its underlying reasons in the literature. In this paper, we find that the following three sources largely contribute to the non-robustness of the IAC based techniques. 1)The global minimum of the cost function lies on a large flat area, which makes any numerical optimization methods difficult to reach it. 2)With noise, many local minimums could appear in the above flat area, which makes any numerical optimization methods quite sensitive to the choice of the initial point since the used method could converge easily to the local minimum close to the initial point. 3) With noise, the point corresponding to the global minimum of the cost function could deviate significantly from the one sought for, thus it is difficult to get the real calibration parameters by minimizing the cost function. In addition, we explore a two-step calibration technique. In this new technique, firstly a plane, which is distant from the origin, is used to obtain an initial solution, then Kruppa equations are used to refine this initial estimation. The experiments on simulated data as well as on real images validate our new technique.