Abstract
In the broadcast of sports events one can commonly see adds or logos that are not actually there - instead, they are inserted into the image, with the appropriate perspective representation, by means of specialized computer graphics hardware. Such techniques involve camera calibration and the tracking of objects in the scene. This article introduces an automatic camera calibration algorithm for a smooth sequence of images of a football (soccer) match taken in the penalty area near one of the goals. The algorithm takes special steps for the first scene in the sequence and then uses coherence to efficiently update camera parameters for the remaining images. The algorithm is capable of treating in real-time a sequence of images obtained from a TV broadcast, without requiring any specialized hardware.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carvalho PCP, Szenberg F, Gattass M. Image-based Modeling Using a Two-step Camera Calibration Method. In: Proceedings of International Symposium on Computer Graphics, Image Processing and Vision, SIBGRAPI’98, Rio de Janeiro, 1998, pp 388–395
Faugeras O. Three-Dimensional Computer Vision: a Geometric ViewPoint. MIT Press, 1993
Jain R, Kasturi R, Schunck BG. Machine Vision. McGraw-Hill, 1995
Tsai R. An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, 1986, pp 364–374
Grimson WEL. Object Recognition by Computer: The Role of Geometric Constraints. Massachusetts Institute of Technology, MIT Press, 1990
Munkelt O, Zierl C. Fast 3-D Object Recognition using Feature Based Aspect-Trees. Technishe Universität München, Institut für Informatik, Germany
Nagao K, Grimson WEL. Object Recognition by Alignment using Invariant Projections of Planar Surfaces. MIT, Artificial Intelligence Laboratory, A.I. Memo no. 1463, February, 1994
Pla F. Matching Features Points in Image Sequences through a Region-Based Method. In: Computer Vision and Image Understanding, vol. 66, no. 3, June, 1997, pp 271–285
Schweitzer H. Computational Limitations of Model Based Recognition. In: http://www.utdallas.edu/~haim/publications/html/modelnpc.html
Zeller C, Faugeras O. Camera Self-Calibration from Video Sequences: the Kruppa Equations Revisited, INRIA Sophia Antipolis, Programme 4, Rapport de Recherche no. 2793, Frévrier, 1996
Gonzalez RC, Woods RE. Digital Image Processing. Addison-Wesley Publishing Company, 1992
Welch G, Bishop G. An Introduction to the Kalman Filter. In: http://www.cs.unc.edu/~welch/media/ps/kalman.ps
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Szenberg, F., Carvalho, P.C.P., Gattass, M. (2001). Automatic Camera Calibration for Image Sequences of a Football Match. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_31
Download citation
DOI: https://doi.org/10.1007/3-540-44732-6_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41767-5
Online ISBN: 978-3-540-44732-0
eBook Packages: Springer Book Archive