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Human action segmentation via controlled use of missing data in HMMs

Peursum, Patrick, Bui, Hung H., Venkatesh, Svetha and West, Geoff 2004, Human action segmentation via controlled use of missing data in HMMs, in ICPR 2004 : Proceedings of the 17th International Conference on Pattern Recognition, IEEE, Washington, D. C., pp. 440-445.

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Title Human action segmentation via controlled use of missing data in HMMs
Author(s) Peursum, Patrick
Bui, Hung H.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
West, Geoff
Conference name International Conference on Pattern Recognition (17th : 2004 : Cambridge, U. K.)
Conference location Cambridge, U. K.
Conference dates 23-26 Aug. 2004
Title of proceedings ICPR 2004 : Proceedings of the 17th International Conference on Pattern Recognition
Editor(s) Kittler, J.
Petrou, M.
Nixon, M.
Publication date 2004
Conference series International Conference on Pattern Recognition
Start page 440
End page 445
Total pages 6
Publisher IEEE
Place of publication Washington, D. C.
Keyword(s) action labeling
hidden Markov models (HMM)
human action segmentation
sliding windows
Summary Segmentation of individual actions from a stream of human motion is an open problem in computer vision. This paper approaches the problem of segmenting higher-level activities into their component sub-actions using Hidden Markov Models modified to handle missing data in the observation vector. By controlling the use of missing data, action labels can be inferred from the observation vector during inferencing, thus performing segmentation and classification simultaneously. The approach is able to segment both prominent and subtle actions, even when subtle actions are grouped together. The advantage of this method over sliding windows and Viterbi state sequence interrogation is that segmentation is performed as a trainable task, and the temporal relationship between actions is encoded in the model and used as evidence for action labelling.
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 0769521282
ISSN 1051-4651
Language eng
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 ©2004, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044634

Document type: Conference Paper
Collections: School of Information Technology
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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.