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A chemotactic-based model for spatial activity recognition

Riedel, D. E., Venkatesh, S. and Liu, W. 2006, A chemotactic-based model for spatial activity recognition, International journal of systems science, vol. 37, no. 13, Special issue : advances in data mining and its applications, pp. 949-959.

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Title A chemotactic-based model for spatial activity recognition
Author(s) Riedel, D. E.
Venkatesh, S.ORCID iD for Venkatesh, S. orcid.org/0000-0001-8675-6631
Liu, W.
Journal name International journal of systems science
Volume number 37
Issue number 13
Season Special issue : advances in data mining and its applications
Start page 949
End page 959
Total pages 11
Publisher Taylor & Francis
Place of publication Essex, U. K.
Publication date 2006
ISSN 0020-7721
1464-5319
Keyword(s) spatial activity recognition
bacterial chemotaxis
interwoven activity recognition
Summary Spatial activity recognition in everyday environments is particularly challenging due to noise incorporated during video-tracking. We address the noise issue of spatial recognition with a biologically inspired chemotactic model that is capable of handling noisy data. The model is based on bacterial chemotaxis, a process that allows bacteria to survive by changing motile behaviour in relation to environmental dynamics. Using chemotactic principles, we propose the chemotactic model and evaluate its classification performance in a smart house environment. The model exhibits high classification accuracy (99%) with a diverse 10 class activity dataset and outperforms the discrete hidden Markov model (HMM). High accuracy (>89%) is also maintained across small training sets and through incorporation of varying degrees of artificial noise into testing sequences. Importantly, unlike other bottom–up spatial activity recognition models, we show that the chemotactic model is capable of recognizing simple interwoven activities.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2006, Taylor & Francis
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044182

Document type: Journal Article
Collection: School of Information Technology
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