Deep learning models for vision-based occupancy detection in high occupancy buildings

Zhang, Wuxia, Calautit, John, Tien, Paige Wenbin, Wu, Yupeng and Wei, Shuangyu 2024. Deep learning models for vision-based occupancy detection in high occupancy buildings. Journal of Building Engineering 98 , 111355. 10.1016/j.jobe.2024.111355

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (23MB) | Preview

Abstract

Accurate occupancy information is crucial for enhancing energy efficiency and reducing carbon emissions in buildings. However, the inherent unpredictability of occupants introduces uncertainties in energy analysis and control strategy development. To address these challenges, this study proposes a vision-based method employing state-of-the-art deep learning models to capture real-time occupancy profiles in crowded indoor spaces. Utilising a self-collected image dataset, various deep learning models, including Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Networks (Faster R-CNN), and different versions of You Only Look Once (YOLO) were trained and evaluated. An experiment was conducted in a lecture room equipped with cameras and environmental sensors to evaluate the performance of each model in terms of precision, computational efficiency, and adaptability to varying occupancy levels during a lecture session. The session included varying occupancy conditions: entering (barely occupied), during the lecture (typical occupancy), and leaving the room (again barely occupied). Among the models tested, YOLOv8x exhibited the best performance in terms of accuracy, while SSD lagged notably. The impact on the detection performance of various locations of camera setups was also explored. Energy simulations revealed that deep learning-based model generated occupancy profiles significantly deviated from conventional “fixed” occupancy profiles, resulting in a 13.45 % variation in predicted heating energy demand. However, compared to the ground truth, these profiles showed minimal variation (up to 6.72 %) for the Faster R-CNN and YOLO models, highlighting their accuracy and robustness. Additionally, although the deep learning-based occupancy profiles generally overpredicted the recorded data, the CO2 concentration trends they predicted aligned closely with the recorded data, unlike the “fixed” occupancy profiles. The findings underscore the importance of realistic occupancy profiles for reliable energy predictions in buildings and demonstrate the potential of the proposed vision-based method for advancing occupancy detection and building energy management.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Architecture
Publisher:	Elsevier
ISSN:	2352-7102
Date of First Compliant Deposit:	17 December 2024
Date of Acceptance:	14 November 2024
Last Modified:	18 Dec 2024 13:00
URI:	https://orca.cardiff.ac.uk/id/eprint/174767

Actions (repository staff only)

Edit Item