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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.

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Title Classifying human actions using an incomplete real-time pose skeleton
Author(s) Peursum, Patrick
Bui, Hung H.
Venkatesh, SvethaORCID iD for 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
Collection: School of Information Technology
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