Gönderen: derindelimavi | Eylül 21, 2013

Novel Patterns and Methods for Zooming Camera Calibration

Novel Patterns and Methods for Zooming Camera Calibration
Andrea Pennisi, Domenico Bloisi, Claudio Gaz, Luca Iocchi, Daniele Nardi
Department of Computer, Control, and Management Engineering
Sapienza University of Rome
via Ariosto 25
00185, Rome, Italy
In Journal of WSCG, volume 21, 2013.

Camera calibration is a necessary step in order to develop applications that need to establish a relationship between image pixels and real world points. The goal of camera calibration is to estimate the extrinsic and intrinsic camera parameters. Usually, for non-zooming cameras, the calibration is carried out by using a grid pattern of known dimensions (e.g., a chessboard). However, for cameras with zoom functions, the use of a grid pattern only is not sufficient, because the calibration has to be effective at multiple zoom levels and some features (e.g., corners) could not be detectable. In this paper, a calibration method based on two novel calibration patterns, specifically designed for zooming cameras, is presented. The first pattern, called in-lab pattern, is designed for intrinsic parameter recovery, while the second one, called on-field pattern, is conceived for extrinsic parameter estimation. As an application example, on-line virtual advertising in sport events, where the objective is to insert virtual advertising images into live or pre-recorded television shows, is considered. A quantitative experimental evaluation shows an increase of performance with respect to the use of standard calibration routines considering both re-projection accuracy and calibration time.


The second video, that contains the “+” sign captured at different zoom levels, is used to refine the calibration
parameters, in particular, the principal point (u;v) and the focal lengths fx and fy, and the radial distortion
coefficients k1 and k2 are considered. For regularizing these parameters an Artificial Neural Network (ANN) based approach [FANN Fast Artificial Neural Networks ] is used. Two different ANNs have been implemented, the former for managing the lower zoom levels, the latter for the higher ones.

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