Automatic Co-Registration of Pan-Tilt-Zoom (PTZ) Video Images with 3D Wireframe Models

Abstract

We propose an algorithm for the automatic co-registration of Pan-Tilt-Zoom (ptz) camera video images with 3D wireframe models. The proposed method automatically retrieves changing camera focal length and angular parameters, due to the motion of ptz cameras by matching linear features between ptz video images and 3d cad wireframe models. The developed feature-matching schema is based on a novel evidence-based hypothesis-verification optimization framework referred to as Line-based Randomized random sample Consensus (lr-ransac). lr-ransac introduces a fast and stable pre-verification test into the optimization process to avoid unnecessary verification of erroneous hypotheses. An evidence-based verification follows to optimally select the ptz camera parameters, where an original line-based approach for full-verification, -exploiting local geometrical cues on the image scene-, evaluates the pre-verified hypotheses. Tests on an indoor dataset produced a 0.06mm error in focal length estimation and rotational errors in the order of 0.18° to 0.24°. Experiments on the outdoor dataset resulted in a 0.07mm error for focal length and rotational errors ranging from 0.19° to 0.30°.