Occupancy Detection | Systems and Informatics Research Lab

This project explored privacy-preserving occupancy estimation and localization in indoor environments using thermal cameras. We developed machine learning and deep learning techniques capable of accurately estimating the number and location of occupants in both sparse and densely populated settings, such as classrooms and auditoriums. By leveraging thermal imagery and advanced feature extraction methods, the proposed system achieved up to 97% occupancy estimation accuracy while preserving occupant privacy.

Details

Lead Student: Soumya Ranjan Sahoo

Domain: Smart Buildings

Year: 2022–2023

Presentation

Dataset

Dataset is avaialble on OSF
Papers are referenced below

Papers

2024

Conference Mapping Thermal Footprints: Occupancy Estimation and Localization in Diverse Indoor Settings with Thermal Arrays

Soumya R Sahoo and Haroon R Lone

In Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS), 2024

Abs DOI

Estimating and locating occupants indoors is crucial for automating operations within buildings. However, current privacy-preserving occupancy estimation systems using thermal cameras have not been thoroughly evaluated in dense settings, such as classrooms or movie theaters, where occupants sit close to one another. Estimating occupancy in such settings presents challenges, as nearby occupants often lead to clusters of thermal signatures, thereby affecting the accuracy of the estimation. In response to these challenges, our work proposes Machine Learning (ML) and Deep Learning (DL) based methods for occupancy estimation and localization, both in dense and sparse settings. The ML method complements existing occupancy estimation techniques by incorporating new manual features, while the DL method performs automatic feature extraction, accurate occupancy estimation and localization. Remarkably, the results demonstrate a significant enhancement in occupancy estimation accuracy of up to 10%, achieving an impressive overall accuracy rate of 97%. Moreover, our evaluation on an edge device confirms the practicality and relevance of the proposed methods in real-world applications.

2023

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

Soumya R Sahoo and Haroon R Lone

In 2023 15th International Conference on COMmunication Systems & NETworkS (COMSNETS), 2023

Abs DOI

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.