Segmentation of intentional human gestures for sports video annotation Chambers, Graeme S., Venkatesh, Svetha, West, Geoff A. W. and Bui, Hung H. 2004, Segmentation of intentional human gestures for sports video annotation, in MMM 2004 : Proceedings of the 10th International Multimedia Modelling Conference, IEEE Computer Society, Los Alamitos, Calif., pp. 124-129. Attached Files Name Description MIMEType Size Downloads venkatesh-segmentationof-2004.pdf Published version application/pdf 429.63KB 220 Title Segmentation of intentional human gestures for sports video annotation Author(s) Chambers, Graeme S. Venkatesh, Svetha orcid.org/0000-0001-8675-6631 West, Geoff A. W. Bui, Hung H. Conference name Multimedia Modelling Conference (10th : 2004 : Brisbane, Qld.) Conference location Brisbane, Qld. Conference dates 5-7 Jan. 2004 Title of proceedings MMM 2004 : Proceedings of the 10th International Multimedia Modelling Conference Editor(s) Chen, Yi-Ping Phoebe Publication date 2004 Conference series Multimedia Modelling Conference Start page 124 End page 129 Total pages 6 Publisher IEEE Computer Society Place of publication Los Alamitos, Calif. Keyword(s) segmentation intentional human gestures sports video annotation Summary We present results on the recognition of intentional human gestures for video annotation and retrieval. We define a gesture as a particular, repeatable, human movement having a predefined meaning. An obvious application of the work is in sports video annotation where umpire gestures indicate specific events. Our approach is to augment video with data obtained from accelerometers worn as wrist bands by one or more officials. We present the recognition performance using a Hidden Markov Model approach for gesture modeling with both isolated gestures and gestures segmented from a stream. 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 0769520847 9780769520841 Language eng Field of Research 080199 Artificial Intelligence and Image Processing 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 ©2004, IEEE Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044637 Document type: Conference Paper Collections: School of Information Technology Open Access Collection Related Links Link Description Connect to published version (restricted access) 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 Fri, 20 Apr 2012, 11:36:48 EST Fri, 27 Apr 2012, 11:59:24 EST Fri, 27 Apr 2012, 12:00:19 EST Fri, 27 Apr 2012, 12:08:52 EST Fri, 27 Apr 2012, 12:17:36 EST Fri, 27 Apr 2012, 12:19:16 EST Thu, 03 May 2012, 13:36:18 EST Tue, 06 Sep 2016, 18:47:21 EST Sat, 21 Oct 2017, 02:02:03 EST Tue, 05 Dec 2017, 12:31:40 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 11 times in Scopus Search Google Scholar Access Statistics: 413 Abstract Views, 217 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:36:48 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.