Artificial Intelligence Enhances Campus Video Deployments—and is Becoming More Affordable

Artificial Intelligence Enhances Campus Video Deployments—and is Becoming More Affordable

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 education 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 solution. 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, 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 integration. An advantage of using edge computing 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 distinguish 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 purposes, 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 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.

The uses in security are well known, but the ability of AI in video to cross department boundaries more than justifies an investment.

Stadium Operations
Before the pandemic began, hundreds or thousands of fans used to pack into stadiums for concerts or athletic events, which presented its own challenges. Adding in new norms such as social distancing and the need for staged entry, it has become essential to equip yourself with the best tools available for crowd control.

AI-driven video analytics can assist in stadium management, offering the ability to monitor and maintain optimal entry scenarios. Video cameras can monitor the line, even sending fans an alert via smartphone when it’s their turn to enter the stadium. AI can also play a role in maintaining social distancing at the entrance. With the addition of queue-management analytics, stadiums can address bottlenecks at the entrance in real time and automatically alert staff when fans need to move to a less congested gate. Other analytics can check whether a fan is wearing a mask and trigger an audio reminder via a nearby speaker.

Campuses depend on concession sales, and long lines can harm profits and frustrate fans. AI-driven analytics can monitor concession lines and send notifications to management to open up more registers or divert customers to a shorter line.

Parking
Parking availability is a precious commodity on school campuses—be it for students, visitors or employees. AI-driven analytics can vastly improve parking management (in addition to security, of course) by monitoring parking spots and conveying availability. If a student late for class sees a sign that says there are 40 spots available, they will know it’s worth their time to enter the lot. If there are only two spots available, however, the student now knows it’s time to move on to an alternative lot. Offering actionable intelligence like this can improve operations and user satisfaction.

Retail or Cafeteria Locations
Campuses often have retail stores like bookstores, convenience stores and cafeterias. By adding AI to a video infrastructure already present in these areas, campuses can use analytics to monitor lines and address any issues, analyze where visitors linger (what products in these areas have caught their attention?) and even monitor what needs to be restocked.

The Numbers: Can Campuses Afford Not to Have AI in Video Surveillance?
AI is growing at a rapid pace within the security industry, particularly as it relates to video, and with an affordable Total Cost of Ownership (TCO) and a positive Return on Investment (ROI), it’s a sound expenditure. Prices for AI and video analytics have come down and can even be included in the price of deep-learning video cameras that are added to a network. The ability to benefit from AI-enhanced video surveillance across departments (security, operations, retail, parking, stadiums, etc.) also dramatically helps improve ROI.

When evaluating an existing video surveillance infrastructure or building one from the ground up, it’s important for campuses to incorporate technology for both today and tomorrow. This means adding AI-driven analytics in some capacity. Be sure to consider TCO and ROI in addition to performance and benefits when evaluating potential products.

This article originally appeared in the March / April 2022 issue of Campus Security & Life Safety.

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