Refining PTZ camera calibration

Due to the increased need for security and surveillance, PTZ cameras are now being widely used in many domains. Therefore, it is very important for the applications like video mosaic generation or automatic surveillance that these camera be accurately calibrated. In this paper, we address the problem of parameter refinement for such pan-tilt-zoom (PTZ) cameras. Use of bundle-adjustment for parameter refinement has widely been adopted in the computer vision field. However, as has been shown by researchers, in presence of noise, this Maximum Likelihood estimate looses its optimality. We propose a novel statistically optimal error function that is shown to experimentally outperform this ML estimate in presence of significant noise. We perform tests on synthetic as well as on real data to verify our method.