Specialist in React, C++, Qt and Python handled this project. Our team members had also knowledge of artificial intelligence methods and related libraries, such as OpenCV. First we get acquainted with the existing temperature measurement system and with possible application sites of the system - factories, hospitals, public buildings. Having this information, we gathered requirements from the client and started to design a web system.

We created the front-end and back-end layers of the system along with REST APIs and ways to store data. Then our team designed computer vision and body temperature measurement algorithms. Thanks to a collection of RGB data we trained machine learning models for face recognition. We created body temperature measurement algorithms based on thermal image projections to RGB images and linked them to recognized individuals. BCF team integrated the web system with algorithmic modules and implemented artificial intelligence solutions. All of the components were thoroughly tested to meet all requirements.

Our team delivered an MVP facility management system connected with a thermal camera and digital camera. Our solution enables the recognition of pre-registered people by an image from the camera. Designed and implemented by us algorithms analyze body temperature and connect data with the face. Due to this innovative solution, the system is able to link body heat information to a specific person.

With the initial database of people, it is easy to control people who regularly enter the building, such as hospital employees.The system offers the ability to efficiently identify fevers for purposes such as pandemic control.

Automated linking of thermal imaging camera data with facial recognition algorithms made it possible to quickly, safely and efficiently identify sick people. Thanks to implemented artificial intelligence solutions like machine learning the system will get better during using it.