Openly accessible

Towards universal and statistical-driven heuristics for automatic classification of sports video events

Tjondronegoro, Dian and Chen, Yi-Ping Phoebe 2006, Towards universal and statistical-driven heuristics for automatic classification of sports video events, in 12th International MuIti-Media ModelIing Conference proceedings : MMM2006, 4-6 Jan. 2006, Beijing, China, IEEE Press, Piscataway, N.J., pp. 43-50.

Attached Files
Name Description MIMEType Size Downloads
chen-towardsuniversalandstatistical-2006.pdf Published version application/pdf 2.08MB 110

Title Towards universal and statistical-driven heuristics for automatic classification of sports video events
Author(s) Tjondronegoro, Dian
Chen, Yi-Ping Phoebe
Conference name International Multimedia Modelling Conference (12th : 2006 : Beijing, China)
Conference location Beijing, China
Conference dates 4-6 January 2006
Title of proceedings 12th International MuIti-Media ModelIing Conference proceedings : MMM2006, 4-6 Jan. 2006, Beijing, China
Editor(s) Feng, Huamin
Yang, Shiqiang
Zhuang, Yueting
Publication date 2006
Conference series International Multimedia Modelling Conference
Start page 43
End page 50
Publisher IEEE Press
Place of publication Piscataway, N.J.
Summary Researchers worldwide have been actively seeking for the most robust and powerful solutions to detect and classify key events (or highlights) in various sports domains. Most approaches have employed manual heuristics that model the typical pattern of audio-visual features within particular sport events To avoid manual observation and knowledge, machine-learning can be used as an alternative approach. To bridge the gaps between these two alternatives, an attempt is made to integrate statistics into heuristic models during highlight detection in our investigation. The models can be designed with a modest amount of domain-knowledge, making them less subjective and more robust for different sports. We have also successfully used a universal scope of detection and a standard set of features that can be applied for different sports that include soccer, basketball and Australian football. An experiment on a large dataset of sport videos, with a total of around 15 hours, has demonstrated the effectiveness and robustness of our
aIlgorithms.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 1424400287
9781424400287
Language eng
Field of Research 080610 Information Systems Organisation
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30009746

Document type: Conference Paper
Collections: School of Engineering and Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Access Statistics: 366 Abstract Views, 110 File Downloads  -  Detailed Statistics
Created: Tue, 14 Oct 2008, 07:01:52 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.