Scalable and Accurate Estimation of Room-Level People Counts from Multi-Modal Fusion of Perimeter Sensors and WiFi Trajectories

Abstract

Estimating the number of people in rooms and zones within commercial buildings are gaining enormous attention for facilitating various domain applications. However, the deployment of state-of-art counting sensors such as camera technologies can be economically in-viable for individual rooms or zones in large commercial and public buildings. Such sensors are also known to be highly intrusive within building deployments. In this paper, we propose a multi-modal fusion method that leverages the accuracy of camera technologies for estimating building-level counts and the non-intrusive and scalability of wireless fidelity (WiFi) trajectory data to estimate room-level counts. This multi-modal fusion method disaggregates the obtained building-level counts by applying a series of data cleaning methods and a two-step probabilistic method. We evaluate the disaggregation method with datasets from a large teaching building, and we benchmark its performance with a state-of-art estimation algorithm and count estimates from raw WiFi trajectories. The obtained evaluation results highlight that the disaggregation algorithm outperforms other estimation methods by a minimum ratio of 35% for all room cases using the Normalized Root Mean Squared Error (NRMSE) metric.