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A statistical-driven approach for automatic classification of events in AFL video highlights Tjondronegoro, Dian, Chen, Yi-Ping Phoebe and Pham, Binh 2005, A statistical-driven approach for automatic classification of events in AFL video highlights, in Proceedings of the twenty eighth Australasian Computer Science Conference (ACSC 2005) Newcastle, Australia, January, 2005, Australian Computer Society, Sydney, N.S.W., pp. 209-218. Attached Files Name Description MIMEType Size Downloads chen-astatisticaldriven-2005.pdf Published version application/pdf 482.95KB 8 Title A statistical-driven approach for automatic classification of events in AFL video highlights Author(s) Tjondronegoro, Dian Chen, Yi-Ping Phoebe Pham, Binh Conference name Australasian Computer Science Conference (28th : 2005 : Newcastle, N.S.W.) Conference location Newcastle, Australia Conference dates January 2005 Title of proceedings Proceedings of the twenty eighth Australasian Computer Science Conference (ACSC 2005) Newcastle, Australia, January, 2005 Editor(s) Estivill-Castro, Vladimir Publication date 2005 Conference series Australasian Computer Science Conference Start page 209 End page 218 Publisher Australian Computer Society Place of publication Sydney, N.S.W. Keyword(s) sports video summarisation semantic analysis self-consumable highlights algorithms AFL Summary Due to the repetitive and lengthy nature, automatic content-based summarization is essential to extract a more compact and interesting representation of sport video. State-of-the art approaches have confirmed that high-level semantic in sport video can be detected based on the occurrences of specific audio and visual features (also known as cinematic). However, most of them still rely heavily on manual investigation to construct the algorithms for highlight detection. Thus, the primary aim of this paper is to demonstrate how the statistics of cinematic features within play-break sequences can be used to less-subjectively construct highlight classification rules. To verify the effectiveness of our algorithms, we will present some experimental results using six AFL (Australian Football League) matches from different broadcasters. At this stage, we have successfully classified each play-break sequence into: goal, behind, mark, tackle, and non-highlight. These events are chosen since they are commonly used for broadcasted AFL highlights. The proposed algorithms have also been tested successfully with soccer video. ISBN 1920682201 9781920682200 Language eng Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified HERDC Research category E1 Full written paper - refereed Copyright notice ©2005, Australian Computer Society Persistent URL http://hdl.handle.net/10536/DRO/DU:30009699 Document type: Conference Paper Collections: School of Information Technology Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     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 Mon, 19 Jan 2009, 13:02:15 EST Thu, 16 Sep 2010, 11:50:11 EST Fri, 16 Mar 2012, 16:38:22 EST Tue, 08 May 2012, 12:39:31 EST Filtered Full Access Statistics: 426 Abstract Views, 8 File Downloads  -  Detailed Statistics Created: Tue, 14 Oct 2008, 07:01:14 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.