Non-Invasive People Counting in Smart Buildings: Employing Machine Learning with Binary PIR Sensors

Azad Shokrollahi, Fredrik Karlsson, Reza Malekian, Jan Persson, Arezoo Sarkheyli-Hägele

2025

Abstract

People counting in smart buildings is crucial for the efficient management of building systems such as energy, space allocation, efficiency, and occupant comfort. This study investigates the use of two non-invasive binary Passive Infrared (PIR) sensors for estimating the number of people in seven office rooms with different people counting intervals. Previous studies often relied on sensor fusion or more complex signal-based PIR sensors, which increased hardware costs, raised privacy concerns, and added installation complexity. Our approach addresses these limitations by utilizing fewer sensors, reducing hardware costs, and simplifying installation, making it scalable and flexible for different room configurations, while also ensuring high consideration of privacy. Additionally, binary PIR sensors are typically part of smart building systems, eliminating the need for additional sensors. We employed several machine learning methods to analyze motion detected by binary PIR sensors, improving the accuracy of people counting estimates. We analyzed important features by extracting event count, duration, and density from sensor data, along with features from the room’s shape, to estimate the number of people. We used different machine learning models for estimating the number of people. Models like Gradient Boosting, XGBoost, MLP, and LGBM demonstrated superior performance for their strong ability to handle complex, non-linear relationships in sensor data, high-dimensional datasets, and imbalanced data, which are common challenges in people counting tasks using PIR sensors. These models were evaluated using performance metrics such as accuracy and F1-score. Additionally, the results show that features such as passage events and the number of detected events, combined with machine learning algorithms, can achieve good accuracy and reliability in people counting.

Paper Citation

in Harvard Style

Shokrollahi A., Karlsson F., Malekian R., Persson J. and Sarkheyli-Hägele A. (2025). Non-Invasive People Counting in Smart Buildings: Employing Machine Learning with Binary PIR Sensors. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 394-405. DOI: 10.5220/0013141800003890

in Bibtex Style

@conference{icaart25,
author={Azad Shokrollahi and Fredrik Karlsson and Reza Malekian and Jan Persson and Arezoo Sarkheyli-Hägele},
title={Non-Invasive People Counting in Smart Buildings: Employing Machine Learning with Binary PIR Sensors},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={394-405},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013141800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Non-Invasive People Counting in Smart Buildings: Employing Machine Learning with Binary PIR Sensors
SN - 978-989-758-737-5
AU - Shokrollahi A.
AU - Karlsson F.
AU - Malekian R.
AU - Persson J.
AU - Sarkheyli-Hägele A.
PY - 2025
SP - 394
EP - 405
DO - 10.5220/0013141800003890
PB - SciTePress