People-counting data can be used in building-control systems to improve comfort and in space management applications to optimize building space. In this work, we consider a combi-sensor with a single-pixel thermopile and passive infrared sensor for people counting. We first develop a thermopile signal model for object temperature measurements under multiple people occupancy. We then propose a people counting method based on: cumulative sum (CUSUM) change detection in the object temperature signal, forming a people count estimate using likelihoods of differential mean temperature in detected changes, and decision fusion with an infrared vacancy sensor. The proposed method is evaluated with data generated using the developed signal model as well as experimental data from a cell office/meeting room environment. 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.@en

We consider the problem of detecting sensor commissioning in the form of determining the sensor layout. We address this problem for single-pixel thermopile sensors, located at the ceiling, that provide remote temperature measurements for people counting applications and HVAC controls. We employ a random forest classifier to determine the deployed layout in an area. For this classifier, we propose spatio-temporal distance features using two-sided cumulative sum recursive least squares (CUSUM RLS) filtering of the thermopile temperature sensor signals. Using sensor data generated with simulated occupancy patterns and a 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%.@en