Spatial activity recognition in a smart home environment using a chemotactic model
Contribuinte(s) |
Palaniswami, M. |
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Data(s) |
01/01/2005
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Resumo |
Spatial activity recognition is challenging due to the amount of noise incorporated during video tracking in everyday environments. We address the spatial recognition problem 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 change motile behaviour in relation to environmental gradients. Through adoption of chemotactic principles, we propose the chemotactic model and evaluate its performance in a smart house environment. The model exhibits greater than 99% recognition performance with a diverse six class dataset and outperforms the Hidden Markov Model (HMM). The approach also maintains high accuracy (90-99%) with small training sets of one to five sequences. Importantly, unlike other low-level spatial activity recognition models, we show that the chemotactic model is capable of recognising simple interwoven activities. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30044626/venkatesh-spatialactivity-2005.pdf http://dx.doi.org/10.1109/ISSNIP.2005.1595596 |
Direitos |
2005, IEEE |
Palavras-Chave | #biological system modeling #biology computing #chemical processes #chemical technology #hidden Markov models #microorganisms #organisms #signal processing #smart homes #working environment noise |
Tipo |
Conference Paper |