Abstract
Building operations are responsible for twenty-seven percent of global carbon dioxide emissions. Currently, building HVAC systems operate at full power at all times. Optimizing them based on the amount and location of people in a building using occupancy detection solutions will reduce the carbon emissions they produce. Building occupancy detection solutions must be low-cost, low-power, high-performance, and privacy-preserving. Our research proposes a non-contact scheme for occupancy estimation using PIR sensors to obtain human body temperatures from a noisy temperature. We collected the ground truth data in the lab by recording the number of occupants detected by numbered PIR sensors in minute intervals. We compared the live data with the heat maps produced by the PIR sensors to determine the accuracy of the heat map. The verified data was then incorporated into a grid search method to determine the direction of occupant movements. In the future, this work will be used to train a model which will be able to accurately determine the occupancy counts of a building.
How to Cite:
, R., , S., , A., , B. & , D.,
(2022) “Occupancy and Direction Estimation Based on Thermal Sensor Data”,
Journal of Student-Scientists' Research 4.
doi: https://doi.org/10.13021/jssr2022.3445
Files
Downloads are not available for this article.