Online Guest Detection in a Smart Home using Pervasive Sensors and
  Probabilistic Reasoning
  Smart home environments equipped with distributed sensor networks are capable
of helping people by providing services related to health, emergency detection
or daily routine management. A backbone to these systems relies often on the
system's ability to track and detect activities performed by the users in their
home. Despite the continuous progress in the area of activity recognition in
smart homes, many systems make a strong underlying assumption that the number
of 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 a
challenge nowadays. Indeed, unlike most (crowd) counting solution which are
based on computer vision techniques, the sensors considered in a Smart Home are
often very simple and do not offer individually a good overview of the
situation. The data gathered needs therefore to be fused in order to infer
useful information. This paper aims at addressing this challenge and presents a
probabilistic approach able to estimate the number of persons in the
environment at each time step. This approach works in two steps: first, an
estimate of the number of persons present in the environment is done using a
Constraint Satisfaction Problem solver, based on the topology of the sensor
network and the sensor activation pattern at this time point. Then, a Hidden
Markov Model refines this estimate by considering the uncertainty related to
the sensors. Using both simulated and real data, our method has been tested and
validated on two smart homes of different sizes and configuration and
demonstrates the ability to accurately estimate the number of inhabitants.
