Tracking-as-recognition for articulated full-body human motion analysis Peursum, Patrick, Venkatesh, Svetha and West, Geoff 2007, Tracking-as-recognition for articulated full-body human motion analysis, in CVPR 2007 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, [Piscataway, N.J.], pp. [1]-[8], doi: 10.1109/CVPR.2007.383130. Attached Files Name Description MIMEType Size Downloads venkatesh-trackingasrecognition-2007.pdf Published version application/pdf 566.70KB 613 Title Tracking-as-recognition for articulated full-body human motion analysis Author(s) Peursum, Patrick Venkatesh, Svetha orcid.org/0000-0001-8675-6631 West, Geoff Conference name Computer Vision and Pattern Recognition. Conference (2007 : Minneapolis, Minn.) Conference location Minneapolis, MN Conference dates 17-22 Jun. 2007 Title of proceedings CVPR 2007 : Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Editor(s) [Unknown] Publication date 2007 Conference series Computer Vision and Pattern Recognition. Conference Start page [1] End page [8] Total pages 8 Publisher IEEE Place of publication [Piscataway, N.J.] Keyword(s) annealing biological system modeling costs hidden Markov models humans layout motion analysis particle filters particle tracking testing Summary This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate. ISBN 9781424411801 1424411807 Language eng DOI 10.1109/CVPR.2007.383130 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 ©2007, IEEE Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044592 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, 11:34:10 EST Fri, 27 Apr 2012, 13:29:41 EST Fri, 27 Apr 2012, 13:30:54 EST Fri, 27 Apr 2012, 13:33:38 EST Thu, 03 May 2012, 11:09:29 EST Tue, 06 Sep 2016, 18:44:44 EST Tue, 05 Dec 2017, 12:31:27 EST Fri, 07 Sep 2018, 15:52:26 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 39 times in Scopus Search Google Scholar Access Statistics: 359 Abstract Views, 613 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:34:10 EST

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