Facilitating PTZ camera auto-calibration to be noise resilient with two images

Abstract

This paper presents a simple but effective technique to calibrate a PTZ (Pan/Tilt/Zoom) camera by using only two images for five intrinsic parameters: focal length, aspect ratio, the principle point coordinates and the distortion coefficient. In our approach, the SCC-SURF (Shape-Color Combined SURF) descriptor is first employed to obtain robust point correspondences in a pair of color images taken before and after the camera undergoing an arbitrary pan-tilt rotation respectively. Based on the radial lens distortion division model, the point correspondences between these two images are applied to calculate the homography and the distortion coefficient simultaneously. The estimated homography is proved more precise with our novel framework CWRLD (Covariance Weighted Ransac under Lens Distortion), which employs a covariance matrix in the presence of feature location noise. Finally, the remaining four intrinsic parameters are solved using directly decomposing estimated homography with a series of Givens rotations. Both synthetic and real data are provided to verify that our proposed technique is precise, convenient, and applicable for online calibration without regard for a specific imaged environment.