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Smart home environments equipped with distributed sensor networks are capableof helping people by providing services related to health, emergency detectionor daily routine management. A backbone to these systems relies often on thesystem's ability to track and detect activities performed by the users in theirhome. Despite the continuous progress in the area of activity recognition insmart homes, many systems make a strong underlying assumption that the numberof occupants in the home at any given moment of time is always known.Estimating the number of persons in a Smart Home at each time step remains achallenge nowadays. Indeed, unlike most (crowd) counting solution which arebased on computer vision techniques, the sensors considered in a Smart Home areoften very simple and do not offer individually a good overview of thesituation. The data gathered needs therefore to be fused in order to inferuseful information. This paper aims at addressing this challenge and presents aprobabilistic approach able to estimate the number of persons in theenvironment at each time step. This approach works in two steps: first, anestimate of the number of persons present in the environment is done using aConstraint Satisfaction Problem solver, based on the topology of the sensornetwork and the sensor activation pattern at this time point. Then, a HiddenMarkov Model refines this estimate by considering the uncertainty related tothe sensors. Using both simulated and real data, our method has been tested andvalidated on two smart homes of different sizes and configuration anddemonstrates the ability to accurately estimate the number of inhabitants.

Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning