Classifying human actions using an incomplete real-time pose skeleton Peursum, Patrick, Bui, Hung H., Venkatesh, Svetha and West, Geoff 2004, Classifying human actions using an incomplete real-time pose skeleton. In Zhang, Chengqi, Guesgen, Hans W. and Yeap, Wai K. (ed), PRICAI 2004 : trends in artificial intelligence : 8th Pacific Rim International Conference on Artificial Intelligence, Auckland, New Zealand, August 9-13, 2004 : proceedings, Springer-Verlag, Berlin, Germany, pp.971-972, doi: 10.1007/978-3-540-28633-2_119. Attached Files Name Description MIMEType Size Downloads Title Classifying human actions using an incomplete real-time pose skeleton Author(s) Peursum, Patrick Bui, Hung H. Venkatesh, Svetha orcid.org/0000-0001-8675-6631 West, Geoff Title of book PRICAI 2004 : trends in artificial intelligence : 8th Pacific Rim International Conference on Artificial Intelligence, Auckland, New Zealand, August 9-13, 2004 : proceedings Editor(s) Zhang, Chengqi Guesgen, Hans W. Yeap, Wai K. Publication date 2004 Series Lecture notes in artificial intelligence ; 3157 Chapter number 119 Total chapters 142 Start page 971 End page 972 Total pages 2 Publisher Springer-Verlag Place of Publication Berlin, Germany Keyword(s) artificial intelligence pose estimation Summary Currently, most human action recognition systems are trained with feature sets that have no missing data. Unfortunately, the use of human pose estimation models to provide more descriptive features also entails an increased sensitivity to occlusions, meaning that incomplete feature information will be unavoidable for realistic scenarios. To address this, our approach is to shift the responsibility for dealing with occluded pose data away from the pose estimator and onto the action classifier. This allows the use of a simple, real-time pose estimation (stick-figure) that does not estimate the positions of limbs it cannot find quickly. The system tracks people via background subtraction and extracts the (possibly incomplete) pose skeleton from their silhouette. Hidden Markov Models modified to handle missing data are then used to successfully classify several human actions using the incomplete pose features. ISBN 9783540228172 3540228179 ISSN 0302-9743 Language eng DOI 10.1007/978-3-540-28633-2_119 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 B1.1 Book chapter Copyright notice ©2004, Springer-Verlag Berlin Heidelberg Persistent URL http://hdl.handle.net/10536/DRO/DU:30044659 Document type: Book Chapter Collections: Faculty of Science, Engineering and Built Environment School of Information Technology 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. Versions Version Filter Type Fri, 20 Apr 2012, 13:23:47 EST Mon, 23 Apr 2012, 14:56:51 EST Mon, 23 Apr 2012, 14:59:16 EST Wed, 02 May 2012, 08:36:23 EST Thu, 20 Nov 2014, 11:01:52 EST Sat, 21 Oct 2017, 02:02:41 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 662 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 13:23:47 EST

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