Inexpensive user tracking using Boltzmann Machines (Type B paper)

Elena Mocanu, Decebal Constantin Mocanu, Haitham Bou Ammar, Zoran Zivkovic, Antonio Liotta, Evgueni Smirnov

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Investigating the presence of a person near a display or in a particular area of the room can lead to improved system capacity by better energy saving or by raising the level of protection of the people (i.e. people privacy which is not assured with other types of sensors like cameras). In this paper [1], we tackle the inexpensive user tracking problem by using the Multi-integrated Sensor Technology (MIST1431), which comes at a low price. At the same time, we are aiming to address the lightweight learning requirements by proposing the extended Factored Conditional Restricted Boltzmann Machine (FCRBM), a form of Deep Learning, which incorporates a novel classification scheme.
The framework contributes on two main directions. The first is a technological one and consists in using a combination of MIST1431 and PIR, two low-cost and low-energy sensors. The second direction is a theoretical one, consisting in the introduction of a novel classification method for time series, namely Extended Factored Conditional Restricted Boltzmann Machines. This new technique builds on FCRBMs by incorporating a label layer and a classification procedure.
Original languageEnglish
Number of pages2
Publication statusPublished - Nov 2015
Externally publishedYes
Event27th Benelux Conference on Artificial Intelligence 2015 - Hasselt, Belgium
Duration: 5 Nov 20156 Nov 2015
Conference number: 27


Conference27th Benelux Conference on Artificial Intelligence 2015
Abbreviated titleBNAIC 2015


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