Surveillance
Entrance monitoring, time tracking, face
and body temperature recognition system
About this project
We had the pleasure to work with a partner who specialized in temperature measurements for a medical project. Our client wanted to leverage his existing hardware and mechanical solutions for imagery temperature inspection to develop a facility management system for entrance monitoring. The goal was to link face recognition for a defined list of users, and their body temperature measurements.
Services
Dedicated Software Development Teams
Technologies
React, C++, Qt, Python, PostgreSQL, Docker, OpenCV, REST, NodeJS, SQL, Jenkins, AWS, Github.
Specialists in React, C++, Qt and Python handled this project. Our team members also had knowledge of artificial intelligence methods and related librariessuch as OpenCV. First, we got 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 the 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 ofthe components were thoroughly tested to meet all ofthe requirements.
Our team delivered an MVP facility management system connected with a thermal and digital camera. Our solution enables the recognition of pre-registered people by an image from the camera. Algorithms designed and implemented by us analyse body temperature and connect data with the face. Due to this innovative solution the system is able tolink body heat information to a specific person.
With the initial database of people’s data it is easy to control who regularly enters 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 improve and better itself as it will be used.
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Due to strong business needs related to projects and organization growth of 5G software development at Nokia, we have started cooperation with BCF Software <...>.
I would like to recommend BCF Software as company and business partner for providing excellent service to Nokia 5G development. I'm pleased to state that BCF provided best service among all the suppliers, delivering the right volume, quality and exceptional dedication to the project.
Herewith I would like to highly recommend the services of Bright Coders' Factory <...>, which are based on our experience in terms of software R&D services that BCF delivers to our global R&D organisation.
The services are reliable, of high quality, cost efficient and with timely deliveries thanks to which Bright Coders' Factory has earned Danelec’s status of Trusted R&D Partner.
Company
TAX ID (NIP):
PL 754 31 26 298
KRS:
0000634606
REGON:
365280382
Our addresses
Opole HQ 45-837, ul. Technologiczna 2
Wroclaw 50-062, ul. PL. Solny 14A/5
Poznan 61-770, ul. Paderewskiego 6/6
Warsaw 00-503, ul. Żurawia 6/12
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