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Human activity learning and segmentation using partially hidden discriminative models

Truyen, Tran The, Bui, Hung H. and Venkatesh, Svetha 2005, Human activity learning and segmentation using partially hidden discriminative models, in HAREM 2005 : Proceedings of the International Workshop on Human Activity Recognition and Modelling, The Conference, HAREM 2005 in conjunction with BMVC 2005, [Oxford, U. K.], pp. 87-95.

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Title Human activity learning and segmentation using partially hidden discriminative models
Author(s) Truyen, Tran TheORCID iD for Truyen, Tran The orcid.org/0000-0001-6531-8907
Bui, Hung H.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Workshop on Human Activity Recognition and Modelling (2005 : Oxford, U. K.)
Conference location Oxford, U. K.
Conference dates 9 Sep. 2005
Title of proceedings HAREM 2005 : Proceedings of the International Workshop on Human Activity Recognition and Modelling
Editor(s) [Unknown]
Publication date 2005
Conference series International Workshop on Human Activity Recognition and Modelling
Start page 87
End page 95
Total pages 9
Publisher The Conference, HAREM 2005 in conjunction with BMVC 2005
Place of publication [Oxford, U. K.]
Keyword(s) patterns
low-level sensory data
hidden Markov models
conditional random field (CRF)
maximum entropy Markov model (MEMM)
Summary Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance. Traditional approaches to this problem typically rely on supervised learning and generative models such as the hidden Markov models and its extensions. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support supervised training is often extremely expensive. In this paper, we propose a new approach based on semi-supervised training of partially hidden discriminative models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that these models allow us to incorporate both labeled and unlabeled data for learning, and at the same time, provide us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart, the partially hidden Markov model, even when a substantial amount of labels are unavailable.
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 ©2005, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044756

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.