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Video Artificial Intelligence (AI) and Deep Learning is now being used to help schools across the U.S. rapidly and cost-effectively enhance safety and security measures in order to help prevent school shootings and other safety issues facing students on a daily basis.

The program was announced recently by Deep North – a pioneer in AI and Deep Learning.

A select number of schools will have the opportunity to deploy and field-test this video AI platform in a novel way, leveraging Deep North’s advanced object and facial recognition technology to detect and prevent a variety of threats to student safety.

The demand for sensible and cost-effective safety measures in schools continues to grow exponentially. The company, therefore, plans to expand into the education sector as a whole in the future.

Using video footage from existing security cameras, the new platform generates real-time, intelligent insights. This equips any facility with a powerful, self-learning sensor that monitors, detects and interprets behaviors and movements by people across physical settings, as well as identifies objects that may present a potential threat.


The AI platform doesn’t replace human judgement, rather, it enhances the staff’s ability to quickly assess what is truly happening and how to best respond.

Deep North maintains important privacy standards, and does not generate, retain, or share any Personal Identifiable Information (PII) of the students or faculty appearing in security video footage. Deep North assigns numeric hashtags instead, as reported by businesswire.com.

Beyond the advantages the technology enables in the event of crises, it can also yield insights that help schools constantly improve their facility’s layout and infrastructure, better manage the flow of student traffic and more.

This patent-pending technology utilizes a combination of cross-camera tracking, hot spots and object alerts to scan crowds in and around school grounds, monitor areas of special concern such as entrances, exits or gathering areas, and identify shapes correlating with weapons or other items of concern. The system can then be set to issue alert notifications when these objects are detected or left abandoned for certain periods of time.