Abstract
This paper presents a method to classify and learn cricket shots. The procedure begins by extracting the camera motion parameters from the shots. Then the camera parameter values are converted to symbolic form and combined to generate a symbolic description that defines the trajectory of the cricket ball. The description generated is used to classify the cricket shot and to dynamically expand or update the system’s knowledge of shots. The first novel aspect of this approach is that by using the camera motion parameters, a complex and difficult process of low level image segmenting of either the batsman or the cricket ball from video images is avoided. Also the method does not require high resolution images. Another novel aspect of this work is the use of a new incremental learning algorithm that enables the system to improve and update its knowledge base. Unlike previously developed algorithms which store training instances and have simple method to prune their concept hierarchies, the incremental learning algorithm used in this work generates compact concept hierarchies and uses evidence based forgetting. The results show that the system performs well in the task of classifying four types of cricket shots.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
H. Hashimoto A. Akutsu, Y. Tonomura and Y. Ohba. Video indexing using motion vectors. In Conference Proceedings of SPIE Visual Communication and Image Processing 1992, pages 1522–1530, 1992.
Douglas H. Fisher. Knowledge aquisition via incremental conceptual clustering. In Jude W. Shavlik and Thomas G. Diettrich, editors, Readings in Machine Learning, pages 267–284. Morgan Kauffman, 1990.
John H. Gennari, Pat Langley, and Doug Fisher. Models of incremental concept formation. Artificial Intelligence, 40:11–61, 1989.
K. Hanna J. R. Bergen, P. Anandan and J. Hingorani. Hierarchical model based estimation. In Proceedings of ECCV92, pages 232–252, 1992.
Mihai Lazarescu, Svetha Venkatesh, and Geoff West. Incremental learning with forgetting (i.l.f.). In Proceedings of ICML-99 Workshop on Machine Learning in Computer Vision, June 1999.
Mihai Lazarescu, Svetha Venkatesh, Geoff West, and Terry Caelli. Combining nl processing and video data to query american football. In Proceedings of IAPR’98, pages 1238–1241, August 1998.
Mihai Lazarescu, Svetha Venkatesh, Geoff West, and Terry Caelli. Using natural language and video data to query and learn american football plays. In Proceedings of ICAPR’98, pages 63–73, nov 1998.
Mihai Lazarescu, Svetha Venkatesh, Geoff West, and Terry Caelli. On the automated interpretation and indexing of american football. In Proceedings of ICMCS’99, pages 802–807, June 1999.
M. Lebowitz. Experiments with incremental concept formation: Unimem. Machine Learning, 2(2):103–138, 1987.
Srinivasan M., S. Venkatesh, and R. Hosie. Qualitative estimation of camera motion parameters from video sequences. Pattern Recognition, 30:593–606, 1997.
J. Park, N. Yagi, K. Enami, and E. Kiyoharu. Estimation of camera parameters from image sequence for model video based coding. IEEE Transactions on Circutis Systems and Video Technology, 3(4):288–296, 1994.
R. E. Reinke and R. S. Michalski. Incremental learning of concept descriptions: A method and experimental results. In J. E. Hayes, D. Mitchie, and J. Richards, editors, Machine Intelligence, volume 11, pages 263–289. 1988.
Y. T. Tse and R.L. Baker. Global zoompan estimation and compensation for video compression. In ICASSP’91, pages 2725–2728, 1991.
S. F. Wu and J. Kittler. A differential method for simultaneous estimation of motion change of scale and translation. Signal Process. Image Comm., 2:69–80, 1990.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lazarescu, M., Venkatesh, S., West, G. (1999). Classifying and Learning Cricket Shots Using Camera Motion. In: Foo, N. (eds) Advanced Topics in Artificial Intelligence. AI 1999. Lecture Notes in Computer Science(), vol 1747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46695-9_2
Download citation
DOI: https://doi.org/10.1007/3-540-46695-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66822-0
Online ISBN: 978-3-540-46695-6
eBook Packages: Springer Book Archive