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For years, video surveillance systems served a single purpose: recording footage that security teams could review after an incident. However, to meet the needs of today’s organizations, video systems must be able to detect suspicious activity, notify the right people, accelerate investigations, and provide operational insights that improve day-to-day business performance.
Powered by advances in artificial intelligence (AI) and computer vision, intelligent video analytics can automatically identify people, vehicles, objects, and situations. These capabilities can help businesses strengthen security while uncovering opportunities to improve efficiency, reduce costs, and make better decisions.
According to the 2026 Stanford AI Index Report, enterprise adoption of AI continues to accelerate as models become more capable and practical for real-world business applications. That momentum is driving innovation across physical security, where AI is transforming video from a passive recording tool into an active source of intelligence.
Intelligent video analytics refers to software that automatically analyzes live or recorded video using artificial intelligence. Instead of simply detecting motion, AI models can identify what’s happening within a scene and determine whether an event requires attention.
For example, modern AI video analytics can identify:
This represents a significant improvement over traditional motion detection, which often generates alerts for rain, shadows, wildlife, or changing lighting conditions. By recognizing the difference between meaningful activity and environmental noise, intelligent analytics help security teams focus on incidents that matter instead of sorting through thousands of false alarms. This, in turn, ensures teams can properly allocate their time to pressing matters instead of having to constantly analyze incoming alerts, helping save operational resources.
AI-powered video analytics has existed for years, but recent advances in AI surveillance have made it more useful, accessible, and better at recognizing objects and behaviors in complex environments. Dedicated analytics processors built into modern cameras allow many AI workloads to run directly at the edge, reducing latency while limiting unnecessary network traffic.
Organizations are also facing new operational challenges. Security teams are often expected to protect larger facilities with fewer resources. Multi-site businesses need greater visibility across hundreds or thousands of locations. Loss prevention teams must investigate incidents more quickly, while operations leaders look for new ways to improve efficiency without increasing headcount.
Rather than simply generating more video, organizations are looking for technology that helps people work smarter.
Early video analytics relied on pixel changes within a designated area to generate an alert. While effective in controlled environments, these systems struggled with shadows, weather, insects, reflections, and countless other variables that created nuisance notifications, which created alert fatigue for operators and less reliable coverage during critical moments.
Modern video analytics solutions take a different approach. Instead of asking whether something moved, AI asks what moved.
Was it a person?
A delivery truck?
An animal?
Is someone entering a restricted area after business hours?
Has a vehicle stopped where it shouldn’t?
Has an individual remained in the same location long enough to indicate potential loitering?
Understanding context allows AI to improve usefulness. Rather than overwhelming security teams with alerts, intelligent systems surface events that are more likely to require attention.
Today’s intelligent video analytics platforms extend well beyond basic security monitoring. Here are five core capabilities that are part of today’s solution:
Nearly every industry can benefit from video analytics for security, but the use cases vary significantly.
Retailers can use AI video analytics to strengthen loss prevention, investigate theft, monitor after-hours activity, and improve customer service through occupancy and queue analytics.
Intelligent video analytics is quickly becoming a mainstream tool for retail operations. According to ECR Retail Loss’ The Utilization of Video Analytics in Retail (2024 Edition), retailers are deploying video analytics not only to strengthen security and loss prevention, but also to improve customer service, monitor operations, and increase efficiency across stores and supply chains.
Quick-service restaurants use analytics to improve drive-thru performance, monitor dining rooms, secure back entrances, and investigate customer incidents more efficiently.
Office buildings benefit from analytics that monitor entrances, parking lots, restricted areas, and visitor traffic while helping security personnel prioritize events.
Schools and universities use AI to improve campus security by monitoring building perimeters, identifying unauthorized access, and helping investigators locate relevant footage quickly following an incident.
Logistics companies rely on analytics to monitor loading docks, secure inventory, track vehicle movement, and monitor high-risk operational areas.
Intelligent video analytics provides numerous benefits for operations teams.
Instead of functioning solely as a security system, video becomes another source of business intelligence.
Not every analytics platform delivers the same level of performance. As organizations evaluate video analytics solutions, several factors deserve careful consideration.
The National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework recommends evaluating AI systems for characteristics such as reliability, safety, security, resilience, accountability, and transparency, principles that apply directly to enterprise video analytics deployments.
Organizations should also consider how well a platform integrates with existing business systems, supports future AI advancements, and provides centralized management across multiple locations.
AI is continuing to evolve rapidly, and intelligent video analytics is evolving with it. The OpenEye platform empowers businesses with intuitive AI features that are redefining how surveillance technology is used. For example, instead of manual methods for locating video, operators can simply input searches like:
“White delivery van that entered the parking lot after 8 p.m.”
“Person who entered through the west entrance carrying a backpack.”
OpenEye Web Services (OWS) can also automate verification of conditions, correlate activity across multiple cameras, and surface unusual events quickly.
As AI capabilities achieve wider adoption, organizations will spend less time searching through video and more time acting on the insights it provides.
At the same time, responsible AI will remain essential. Organizations need confidence that AI systems are secure, transparent, and deployed appropriately. The Cybersecurity and Infrastructure Security Agency (CISA) recommends applying Secure by Design principles throughout the AI lifecycle to reduce risk and strengthen trust in AI-enabled systems.
Video has always been valuable because it captures what happened. Today, it can help organizations understand what’s happening now, and in many cases, respond before a situation escalates.
That’s why intelligent video analytics has become one of the most important advancements in modern physical security. By combining AI with cloud-managed video, organizations can strengthen security, reduce investigation times, improve operational visibility, and make better-informed decisions across the business.
As AI capabilities continue to mature, the organizations that benefit most will have systems that help people find needed information quickly and turn video into meaningful action.
Interested in learning more? Explore OpenEye’s AI-powered video analytics solutions today.
Intelligent video analytics uses artificial intelligence and computer vision to automatically analyze live or recorded video. Rather than simply recording footage, the system identifies people, vehicles, and objects to generate actionable alerts and insights.
Traditional motion detection reacts to changes in pixels, which often leads to false alarms caused by shadows, weather, or animals. AI video analytics recognizes and classifies objects, making alerts far more useful.
Retail, education, restaurants, financial institutions, convenience stores, cannabis, manufacturing, logistics, commercial real estate, and multi-family property management all use intelligent video analytics to improve security, accelerate investigations, and gain operational insights.
Yes. Modern AI models distinguish between people, vehicles, animals, and environmental activity, helping eliminate many nuisance alerts that traditional motion detection cannot filter effectively.
Organizations should evaluate scalability, cybersecurity, cloud management capabilities, intelligent search and alerting, integration options, and the vendor’s long-term AI roadmap.
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