A computer vision-based model for occupancy detection using low-resolution thermal images

TL;DR

This paper presents a privacy-preserving occupancy detection model using low-resolution thermal images and transfer learning with YOLOv5, achieving high accuracy while reducing computational resources.

Contribution

The study introduces a novel occupancy detection approach that leverages low-resolution thermal images and transfer learning, addressing privacy concerns and computational efficiency.

Findings

Achieved high precision, recall, mAP50 values close to 1.000.

Demonstrated effective occupancy detection with non-invasive thermal imaging.

Reduced computational resource requirements compared to traditional methods.

Abstract

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.