AI helps remote video monitoring scale in high-traffic environments

For security integrators and consultants supporting high-traffic venues, one of the biggest operational challenges is not a lack of video. It is too much of it. When large numbers of cameras generate constant motion-based alerts, security teams can struggle to separate routine activity from genuine threats.
 
That challenge is becoming more acute in environments such as entertainment venues, residential communities, airports, and other critical infrastructure, where operators are expected to monitor many sites at once while maintaining fast response times.
 
Recent deployment experience in Puerto Rico points to a growing role for AI-enabled analytics integrated with video management systems to reduce nuisance alarms and improve centralized monitoring.

Centralized monitoring takes shape

According to Johana Arias, Sales Director in Milestone Systems, the project centered on replacing fragmented monitoring approaches with a centralized operation that could serve multiple customers simultaneously. "Instead of deploying standalone systems for each client, Genesis developed a centralized monitoring operation capable of simultaneously serving multiple."
 
The setup combined Milestone’s XProtect Corporate video management software with cloud-based AI analytics from Actuate for remote video monitoring.
 
The operational issue was clear. Before AI-driven analytics were added, operators had to handle more than 96,000 motion detection alerts per day from thousands of cameras. In practice, that meant genuine threats could be buried under an unmanageable volume of notifications.
 
The main objective, Arias said, was to deploy technology capable of "filtering out noise, reducing false alarms, and enabling a smaller team of operators to efficiently monitor more locations and client types without compromising response times or overall service quality."

Open architecture supports integration 

From a systems integration perspective, one of the more relevant aspects of the project was the architecture. Using an open platform VMS allowed cameras from different manufacturers, including Axis Communications and Uniview, to be brought into the same environment while also connecting AI analytics through supported protocols and network bridge capabilities.
 
This made it possible to process video streams from distributed sites using AI models for intrusion detection, loitering detection, weapon detection, and crowd monitoring. Alerts verified by AI were then sent back into the VMS event workflow, giving operators more actionable information.
 
The result was a notable drop in operator-facing alarms. Arias said Genesis saw "a 62% reduction in alerts reaching operators, dropping from roughly 96,000 notifications per day to about 37,000 actionable events after implementing AI analytics with Actuate."

Why the alert reduction mattered 

Bandwidth management was also an important factor. The system minimized data use by relying on JPEG snapshots when motion occurred, while reserving higher-quality streams for more demanding analytics such as firearm detection.
 
That approach helped reduce load without requiring major changes at customer sites. Importantly, the deployment was completed without additional hardware being installed on premises.
 
For integrators, this points to a practical lesson. In many multi-site environments, centralized and cloud-based analytics can improve scalability when on-site hardware expansion is undesirable or cost-prohibitive.
 
In this case, the centralized model supported monitoring across thousands of camera streams without the need for extra processing hardware in the field.

Managing peak-period response

Performance during peak attendance periods also depended on more than analytics alone. Operators used a real-time map interface to track movement across camera views, activate on-site speakers for live audio warnings, and coordinate dispatch from a unified platform.
 
That integration of video, analytics, audio intervention, and response workflows is likely to be of particular interest to consultants designing systems for event-driven environments.
 
The project also highlighted the continued importance of resilience. Puerto Rico’s infrastructure challenges, including fragile power and uneven communications coverage, required a redundant design with multiple video servers, automatic failover, and a hybrid network approach combining fiber, wireless, and LTE depending on site conditions.

Infrastructure lessons for integrators

For security professionals, the broader takeaway is that AI analytics are most valuable when deployed as part of an open, tested, and operationally grounded architecture.
 
As Arias put it, "Milestone’s open architecture allows integrators to evaluate multiple technologies and confidently deploy the solutions that best meet customer requirements."
 
In crowded, high-alert environments, that flexibility may increasingly determine whether remote monitoring operations can scale without losing effectiveness.