Modeling Count Data from Multiple Sensors: A Building Occupancy Model

Abstract

Knowledge of the number of people in a building at a given time is crucial for applications such as emergency response. Sensors can be used to gather noisy measurements which when combined, can be used to make inferences about the location, movement and density of people. In this paper we describe a probabilistic model for predicting the occupancy of a building using networks of people-counting sensors. This model provides robust predictions given typical sensor noise as well as missing and corrupted data from malfunctioning sensors. We experimentally validate the model by comparing it to a baseline method using real data from a network of optical counting sensors in a campus building.