Classifying and learning cricket shots using camera motion

Lazarescu, Mihai, Venkatesh, Svetha and West, Geoff 1999, Classifying and learning cricket shots using camera motion, in Advanced topics in artificial intelligence : 12th Australian Joint Conference on Artificial Intelligence, AI'99, Sydney, Australia, December 6-10, 1999 : proceedings, Springer, New York, N. Y., pp. 13-23.

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Title Classifying and learning cricket shots using camera motion
Author(s) Lazarescu, Mihai
Venkatesh, Svetha
West, Geoff
Conference name Australian Joint Conference on Artificial Intelligence (12th : 1999 : Sydney, N. S. W.)
Conference location Sydney, N. S. W.
Conference dates 6-10 Dec. 1999
Title of proceedings Advanced topics in artificial intelligence : 12th Australian Joint Conference on Artificial Intelligence, AI'99, Sydney, Australia, December 6-10, 1999 : proceedings
Editor(s) Foo, Norman Y.
Publication date 1999
Conference series Australian Joint Conference on Artificial Intelligence
Start page 13
End page 23
Total pages 11
Publisher Springer
Place of publication New York, N. Y.
Keyword(s) camera motion parameters
cricket
trajectory
incremental learning algorithm
classification
Summary 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 bell. 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 prime 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.
ISBN 3540668225
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©1999, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044891

Document type: Conference Paper
Collection: School of Information Technology
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