Design of a Breach Detection System for Social Distancing

• Main Authors: Guico, Maria Leonora, Oppus, Carlos M, Monje, Jose Claro N, Kwong, John Chris T, Ngo, Gwendolyn, Belarmino, Mark Daniel, Mamaril, Cris Emmanuel Cirglen, Ngo, Genevieve C
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: social distance; object recognition; perspective transformation; breach map; Electrical and Computer Engineering; Medicine and Health Sciences
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/129; https://doi.org/10.1109/ICOCO53166.2021.9673501
• Institution: Ateneo De Manila University

The pandemic caused by the 2019 novel coronavirus introduced essential health protocols for everyone's safety. One of which is maintaining a social distance of at least 1 meter as per the guideline set by World Health Organization (WHO). Currently, most spaces were designed prior to the implementation of the social/physical distancing protocol. This project aims to design and develop a detection system utilizing closed-circuit television cameras, to identify spaces where there is a possible breach in the social distancing protocol. The system will generate discrete data to be queried for tabulation, and analysis. The system will also generate a breach map, which indicates the area in the CCTV footage where increasing breaches occur and are marked in increasing color intensity. The system utilized the YOLO V3 object detection algorithm in identifying an object to be human. The system utilized perspective transformation and Euclidean distance estimation in approximating distance for the social distancing protocol. In summary, the human detection accuracy of the system is ≃ 91%, processing at a rate of 30 frames per second in real-time.