Robust control point estimation with an out-of-focus camera calibration pattern

Abstract

The calibration of a zoom lens camera depends on the precision of the localization of control points. At a long focal length, the narrow depth-of-field (DOF) causes defocused blurring and an inevitable decrease in accuracy in control points localization. In particular, the camera calibration requires multiple control points defined on calibration patterns acquired at various camera angles. However, clear pattern images are difficult to obtain owing to the narrow DOF. We propose a robust and intuitive method to accurately estimate the control points in blurred images. To obtain control points that are less affected by blurring, we dynamically varied the circle size in the patterns and identified the local maximum point using the intensity gradient of accumulated concentric circles. This approach is robust to blurring and can be employed at all zoom levels. In our experiments, the error of the control point estimation was measured while varying the angles of the calibration patterns and the degree of blurring. Compared with the conventional checker pattern method, the performance of the proposed method in the estimation of the control points was better and its related camera parameters with severely defocused images were settled. © 2020 Elsevier B.V.