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 telep-resence 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.