Modeling the variation of the intrinsic parameters of an automatic zoom camera system using moving least-squares

The accuracy of machine vision systems is highly depending on the correct estimates of the camera intrinsic parameters. This accuracy is needed in numerous applications like telepresence and robot navigation. In this work, a novel technique is proposed based on the moving least-squares approach, to model the variation of the camera internal parameters as a function of focus and zoom. Compared to a previous technique using a global least-squares regression scheme with bivariate polynomial functions, the new method results in a huge reduction of the mean estimation error. In addition, validation tests show that the estimated values of the interpolated data are enhanced substantially even with a small number of measured focus and zoom settings. Consequently, fewer measurement points are needed to obtain an accurate model of the internal parameters of a zoom camera system.