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A chemotactic-based model for spatial activity recognition
journal contribution
posted on 2006-01-01, 00:00 authored by D Riedel, Svetha VenkateshSvetha Venkatesh, W LiuSpatial 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.
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Journal
International journal of systems scienceVolume
37Issue
13Season
Special issue : advances in data mining and its applicationsPagination
949 - 959Publisher
Taylor & FrancisLocation
Essex, U. K.ISSN
0020-7721eISSN
1464-5319Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2006, Taylor & FrancisUsage metrics
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