Occupancy counting in dense and sparse settings with a low-cost thermal camera

Abstract

Occupancy detection and counting are crucial to automate buildings' lighting, heating, and cooling systems. However, most existing occupancy detection and counting techniques are underperforming or privacy-averse. In this work, we propose an adaptive blob filtering algorithm for processing thermal images and evaluate several classification algorithms for occupancy counting. We evaluate the performance of the proposed algorithm using a low-cost privacy-preserving thermal camera under both sparse and dense occupancy settings. Our results show that the proposed algorithm improves occupancy counting results significantly compared to two existing baselines, resulting in an average accuracy of 84.5% under dense classroom settings.