Page 52 - Security Today, April 2022

“Analytics that use AI can help identify people (instead of a curious animal) loitering around a campus at 3 a.m. It can assist in investigations: quickly finding, for example, a burglary suspect reported as wearing a yellow shirt. It delivers invaluable situational awareness since campus police cannot be everywhere at once to monitor for incidents.”
While many in educa- tion have the desire to be early adopters of new technologies, budget constraints and path dependency (sticking with the same old technology, or none at all, because it’s too difficult to change) are often obstacles that interfere with deploying the latest advancements.
Campuses should, however, consider incorporating artificial intelligence (AI) and analytics into their video surveillance solu- tion. AI has a variety of uses that can benefit all departments, not just security—making it justifiably affordable.
Technically Speaking: How is AI Used in Video Surveillance?
Video analytics uses AI and deep learning to create searchable, actionable and quantifiable intelligence from live or recorded video and develop rules on how to respond. This means that AI can use video footage to recognize and extract objects (as well as information) about the type and attributes of these objects. These can include people, loiterers, numbers of people and people counting, cars, animals,
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travel direction, and the like. Alerts are sent to a preconfigured recipient, helping quickly notify a responsible party who can then act on the information.
Video analytics can be run on the edge at the device (for example, in a deep learning video surveillance camera) or can be added to a video management solution via an inte- gration. An advantage of using edge comput- ing in the camera is that the information processing power sits as close to the source as possible. Edge analytics tends to have both greater accuracy and the ability to distin- guish between multiple classes of objects, which immediately reduces the rate of false positives and saves unnecessary investigation time. As a result, edge analytics can deliver a more appropriate and timely response.
In a traditional model—when analytics takes place on a server—video is often compressed before being transferred. However, this often results in the analysis being undertaken on video of degraded quality. Additionally, when analytics is centralized on a server and when more cameras are added to the solution, more data is transferred, and more servers are needed to handle the analytics. Deploying powerful
analytics at the edge means that only the most relevant information is sent across the network, reducing the burden on bandwidth and storage.
Regardless of whether AI-driven analytics are on the edge or in a server, they can vastly improve campus security and operations.
How Does AI Help Security and
Other Departments?
AI delivers valuable information for any industry, but it’s particularly helpful for K–12 and higher-education campuses. Campus video cameras are primarily for security pur- poses, and AI can play an important role in enhancing safety. Analytics that use AI can help identify people (instead of a curious animal) loitering around a campus at 3 a.m. It can assist in investigations: quickly find- ing, for example, a burglary suspect reported as wearing a yellow shirt. It delivers invalu- able situational awareness since campus police cannot be everywhere at once to mon- itor for incidents.
The uses in security are well known, but the ability of AI in video to cross department boundaries more than justifies an invest- ment.