In 2023, the world witnessed an unprecedented surge in advancements and applications of artificial intelligence (AI), establishing it as the “year of AI.” Across diverse sectors, from security to finance, education to entertainment, the transformative power of AI became increasingly evident and part of the mainstream conversation.
Breakthroughs in natural language processing, computer vision, and machine learning algorithms propelled innovative solutions and reshaped many industries, including the security industry.
In the world of video surveillance, analytical AI brings capabilities which previously would have required a large team to implement, improving both security and operational efficiencies. Cloud computing allows customers to access specialized, high-intensity AI algorithms for which on-prem data processing would be impractical, and to scale easily no matter how much data is involved.
A number of the top trends covered in the 2024 Trends in Video Surveillance report are connected to the increase in AI technologies and uses within the security industry. Read on for a better understanding of how AI is already being applied in surveillance.
AI enhanced video search
No matter how dedicated security personnel are, most conventional security video isn’t watched in real time – instead, it takes painstaking scrubbing to spot and analyze suspected incidents after the fact. And many security and safety concerns will inevitably be missed. AI offers the ability to search all video in a system at once, greatly accelerating the time it takes to find specific events, improving system efficiency.
At the simplest level, AI algorithms take video at an image level and generate metadata from it on a frame-by-frame basis. In other words, the AI takes video in and converts it to information: there is a vehicle here, there is a person here, there is an object. This metadata attached to the video makes it possible to use natural-language search for specific images, or to generate alerts based on pre-designated images.
Industry- or site-specific safety applications
AI-powered video analytics can accurately identify specific objects or circumstances and trigger alerts to predesignated managers for immediate attention. These trained AI models have many uses, and many that can be applied to specific industries or sites. AI analytics include, but are not limited to: gun detection, slip-and-fall detection, worker safety monitoring, queuing, fire and smoke detection, and face detection.
Worker safety monitoring, for example, is useful In industries or settings where people are required to observe safety protocols, such as the wearing of personal protective equipment (PPE) such as hard hats or reflective vests. AI analytics simplify monitoring and improve compliance. Managers or shift leaders may not be available to spot every lapse firsthand, but with AI, workers and visitors on a job site or in a warehouse can be quickly reminded to use protective gear—reducing risk and safety violations.
AI models are also being trained to spot firearms in a work or school setting, enabling continuous monitoring for immediate response. Businesses and schools that implement gun detection can use AI to alert the necessary authorities. All AI systems can produce false positives — for example, by identifying a toy gun as a real one — but ultimately pass the information on to a human to decide what action is needed.
Valuable business insights
The intersection of surveillance and AI isn’t confined to the most conventional use of surveillance as a tool for observing and documenting safety or security. AI can expand the capabilities of video cameras to help decision makers understand their own business with useful data. AI analytics can provide data-driven insights such as foot traffic patterns and customer counts, peak wait times, and parking occupancy. This data allows businesses to make informed decisions about staffing, training, and operations.
AI-supported video monitoring
One non-intuitive fact: The output of most video security cameras is never watched. Why? Because cameras are often recording on a just-in-case basis, with more potential video streams than there are person-hours allotted to watch it. In most cases you don’t expect to need the camera’s output.
What that means, though, is that cameras can also be essentially forgotten – and therefore ignored when they shouldn’t be. With a centralized video management system (VMS) informed by AI, video surveillance can be filtered for predetermined events and alert human observers or security personnel of these events. That can mean movement in general, or spotting a particular vehicle, or noting unusual behavior. With that kind of always-on filtering, camera streams don’t suffer from the effects of human boredom, inattention, or task-splitting, and false positive alarms can be spotted before they waste anyone’s time.
As mentioned in the 2024 Trends report, too, AI filtering can greatly reduce the incidence of false positive alarms, which contributes to making professional video monitoring more affordable and cost-effective.
Future-proof surveillance systems
As the world grows accustomed to AI, the power it brings will slowly become the expected usable minimum set of capabilities – table stakes. One writer predicts that 2024 will be the year that AI slips into the background, disappearing for the large part as it’s simply integrated into the systems that people rely on. Video surveillance without the efficiencies provided by AI will become ever less attractive, as its limitations become more evident.
Cloud video surveillance not only provides access to the latest AI capabilities, but also provides a future-proof option to add new features as AI in security continues to advance.
There are many other benefits to cloud video surveillance and there’s never been a better time to consider your current surveillance systems, and consider whether it’s set to take advantage of both today’s AI capabilities, and the ones on the horizon.
Eagle Eye Networks provides the AI-powered solutions described in this article. Schedule a demo to learn more.