A Hybrid Model to Ensure Biosecurity during Pandemic Situations Using OpenCV

Abstract

This paper presents a hybrid model consisting of hardware, software subsystems and publicly available trained feature sets. The developed hybrid model is useful in automated contactless collection and analysis of employees’ and visitors’ data in an organization especially during pandemic situations to ensure biosecurity. Such data include temperature and face mask status. If the set norms are not satisfied, the entry into the premises will be restricted or denied. The status is also updated in the corresponding record in the organization database. The hardware subsystem includes an arduino nano, sensors and audio visual alarms. The software subsystem was developed using OpenCV in Python and VSCode editor. Both offline and real time implementations were carried out. The model was validated using real time images and online data sources. The system was tested and found to work satisfactorily under practical input conditions.