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2006
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A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment

10 years 1 months ago
A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment
To tackle the problem of increasing numbers of state transition parameters when the number of sensors increases, we present a probabilistic model together with several parsinomious representations for sensor fusion. These include context specific independence (CSI), mixtures of smaller multinomials and softmax function representations to compactly represent the state transitions of a large number of sensors. The model is evaluated on real-world data acquired through ubiquitous sensors in recognizing daily morning activities. The results show that the combination of CSI and mixtures of smaller multinomials achieves comparable performance with much fewer parameters.
Dinh Q. Phung, Dung T. Tran
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Dinh Q. Phung, Dung T. Tran
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