ANTI-CONTACT SAFETY MEASURES
The first italian school governed by artificial intelligence
V-App Smart Integration Platform, the digital solution that detects distances and reports social gatherings
THE CASE – The Istituto Superiore Bona in Biella is the first school in Italy to adopt the V-App Social Distancing Monitoring. The system was tested for the school’s final exams in order to evaluate and assess its effectiveness in a complex environment. As a matter of fact, the school offers areas that are dedicated to different uses (laboratories, classrooms, gyms, etc.) and where, albeit observing the anti-Covid-19 safety requirements, pupils and school staff have now returned with greater ease and confidence.
Through the use of “intelligent” video cameras positioned in strategic points around the Institute, V-App monitors the safe distance between people, as well as the degree of crowding in common areas and specifically sensitive areas such as entrances and toilets.
A non-invasive and privacy-friendly monitoring tool
The V-App platform is a highly reliable, non-invasive and totally discreet monitoring solution – there is nothing to remember, no device to wear, which you can lose or forget. The operating process of the various V-App application components and data processing comply with the requirements of the GDPR and privacy regulations.
An “intelligent, preventive and educational” safeguard
The V-App solution offers a complete and innovative archetype, from the technological point of view as well as regarding the perception by students and school staff. V-App overcomes the approach of the most classic video surveillance systems, introducing artificial intelligence algorithms based on events, forms, rules and thresholds, offering a system that can identify “risk situations” in a context of progressive “learning”.
The results in the period of the high school exams
TR – Traffic Reported
Number of presences detected and managed by the monitoring system
SDE – Social Distancing Event
Number of Social Distancing events
MD – Mask Detection
Mask Detection event distribution
(PDF file 928 Kb)