Single-Pixel Thermopile Sensors for people counting applications

Abstract

People counting data in offices is used in many applications like HVAC system control and space management to increase comfort, decrease energy consumption and optimise space utilisation. In contrast to past approaches using imaging modalities that tend to be either expensive or intrusive, we consider single-pixel thermopile sensors for people counting. These sensors may already be deployed as part of a smart lighting system to provide temperature data for HVAC controls. Firstly, a statistical sensor model for thermopile temperature measurements is proposed. The proposed people counting method enhances the CUSUM RLS algorithm to estimate temperature change caused by people entering or leaving. We estimate mean temperature changes upon detection of an occupancy event, and then estimate the people count using a maximum likelihood on the estimated temperature change. Finally, PIR vacancy data is merged with the people count estimation to increase accuracy. We obtain an average counting error of 0.11 and 0.19 for 90% of the instants respectively when considering 15 minute windows for simulated and experimental datasets. A second aspect of the thesis considers the problem of commissioning plan detection. We leverage the two-sided CUSUM signals to address this problem. The two-sided CUSUM scores for a pair of sensors are used to calculate similarity measures; these features are used in a Random Forest Classifier to detect commissioning changes of the sensor pair. Using simulated data with the thermopile signal model, we show that the proposed method achieves a true positive rate (determining the correct layout) of 90.2% and false positive rate of 1.3%.