A computer vision-based model for occupancy detection using low-resolution thermal images
This work addresses privacy issues in occupancy detection for building energy management, but it is incremental as it applies an existing method (YOLOv5) to a new data type (thermal images).
The study tackled occupancy detection for HVAC systems by developing a computer vision model using low-resolution thermal images to address privacy concerns, achieving near-perfect performance with precision, recall, and mAP50 values approaching 1.000.
Occupancy plays an essential role in influencing the energy consumption and operation of heating, ventilation, and air conditioning (HVAC) systems. Traditional HVAC typically operate on fixed schedules without considering occupancy. Advanced occupant-centric control (OCC) adopted occupancy status in regulating HVAC operations. RGB images combined with computer vision (CV) techniques are widely used for occupancy detection, however, the detailed facial and body features they capture raise significant privacy concerns. Low-resolution thermal images offer a non-invasive solution that mitigates privacy issues. The study developed an occupancy detection model utilizing low-resolution thermal images and CV techniques, where transfer learning was applied to fine-tune the You Only Look Once version 5 (YOLOv5) model. The developed model ultimately achieved satisfactory performance, with precision, recall, mAP50, and mAP50 values approaching 1.000. The contributions of this model lie not only in mitigating privacy concerns but also in reducing computing resource demands.