AI and video management in genetec
Genetec’s Security Center offers a unified view of physical security and video. It was designed to unify video, access control, and more in a single pane. Security Center integrates cameras and alarms and it can host AI modules that extend traditional video management. By adding AI, operators gain tools that reduce noise and raise meaningful detections.
AI modules improve real-time object detection and classification by filtering raw outputs into actionable events. They tag people, vehicles, and objects and they prioritise what needs attention. In tests, AI-enhanced surveillance systems reduced false alarms by up to 90%, and they increased incident detection rates by roughly 40–60%. These figures show why operators demand smarter processing of video and metadata. The net effect is fewer meaningless alerts and more time for qualified response.
Operators benefit when AI integrates tightly with access control and other sensors. AI can correlate an entry badge with a person seen on camera and then verify an event. This reduces verification time and improves audit trails. For organisations under regulatory pressure, keeping processing local matters. Systems that avoid sending video to the cloud help teams maintain control and address EU privacy rules. visionplatform.ai supports on-prem workflows and provides an on-prem Vision Language Model to convert visual events into searchable text. That lets teams search across recorded timelines using human terms.
For those planning deployments, think about hardware capacity and bandwidth. AI models add CPU or GPU load and they can increase storage needs for enriched metadata. Yet benefits are clear. AI brings faster verification, reduced manual review, and more reliable decision-making in the control room. When implementing, choose AI that integrates with Security Center and that exposes events in a format your operational tools can consume. This approach helps security teams scale without adding headcount and ensures the VMS becomes a platform for assisted action not just recording.
milestone systems and agentic ai integration
Milestone XProtect has long been prized for its open architecture and SDK support. The platform allows integrators and vendors to extend the VMS with third-party modules. As a result, Milestone’s ecosystem now supports agentic approaches that place reasoning on top of detections. Agentic AI can act, recommend, and assist control room operators in structured ways.
Agentic AI refers to systems that do more than flag events; they reason, prioritise, and can follow simple policies. In a Milestone deployment, an ai agent can query device information through the Milestone API, access video streams, and provide structured outputs. For example, a test deployment with coram.ai integrated into XProtect reported a 30% reduction in manual video review time. That kind of efficiency gains directly lowers operational load and improves response times for mission-critical monitoring.
VisionPlatform concepts such as an ai agent suite for milestone xprotect model how vendors can package search, reasoning, and action in one bundle. The visionplatform.ai agent suite for milestone exposes XProtect events so agents can reason over them and support assisted decision-making on top of existing analytics. Operators can interact with video and timelines using natural language, and the agents can provide recommendations based on prior context. This cuts the time to locate video evidence and streamlines incident workflows.
Milestone VMS AI Agent modules can be designed to run fully on-site. That avoids sending video to the cloud, which matters for information security and compliance. Meanwhile, an on-prem vision language model (VLM) can translate scenes into structured text so agents can search across cameras and timelines using short queries. Introducing three tightly integrated AI agents — search, reasoning, and actions — allows the system to verify events, suggest steps, and automate low-risk responses. In this way, Milestone XProtect becomes a platform not just for recording but for active operational support.
AI vision within minutes?
With our no-code platform you can just focus on your data, we’ll do the rest
use cases for ai-driven surveillance
AI-driven surveillance unlocks practical use cases that go beyond simple motion detection. For perimeter breach detection, AI can identify unauthorised entry and loitering near sensitive gates and it can correlate detections with badge events. That leads to faster alerts and less time spent on false positives. For more context, see our work on perimeter breach detection that explains how AI refines boundary monitoring for real sites.
Traffic and crowd monitoring is another productive use case. AI counts vehicles and people, analyses flow, and flags anomalous behaviour in crowded venues. AI can provide vehicle detection classification and people-counting metrics for operations and planning. The benefit is measurable. When AI classifies and filters routine passages, response teams receive only the events that matter and they can allocate resources efficiently.
Behavioral analytics add an extra layer. For example, systems can flag loitering near loading docks or detect when a person leaves an object behind. These pre-defined object classes and behavioural patterns are useful in airports, campuses, and industrial sites. If an AI model was trained with site-specific examples, detection accuracy improves and maintenance effort falls. That is why many integrators prefer models that can be tuned to local conditions.
Quantified benefits show up in operations. AI-driven pipelines reduce the volume of manual review, speed up alert triage, and improve situational awareness for control room operators. By converting video into structured descriptions, a vision language model enables operators to search across cameras and timelines using plain queries and thus locate video evidence faster. For forensic tasks, see our forensic search in airports page for how natural queries return precise clips. Overall, AI systems help teams manage large volumes of video and they streamline routine investigations so that human attention focuses where it adds the most value.
automation and response times for security needs
Automation shifts the control room from reactive to proactive. Automated alert generation and prioritisation ensure that critical incidents reach the right person immediately. AI can score alerts by risk, combine corroborating sensors, and pass only validated incidents to on-duty guards. This reduces noise and shortens time-to-action for mission-critical events.
Integration of AI alerts with guard patrol and control-room dashboards is essential. When an alert arrives, the system can display a recommended action, show a short, verified clip, and provide nearby camera angles. A visionplatform.ai control room AI agent provides this type of contextual verification and suggested steps. Operators then decide quickly, supported by clear evidence and recommended next actions. That assists decision-making and preserves human oversight where it matters most.
As a result, teams measure faster response times and improved efficiency. AI that verifies events reduces the number of unnecessary dispatches and it increases the percentage of validated incidents. For routine, low-risk scenarios, automated workflows can execute predefined responses while maintaining full control and audit logs. These workflows can pre-fill incident reports, notify external teams, or trigger connected control room software. Such automation helps security teams scale and keeps handling consistent.
Better tools also mean better training and fewer mistakes. When AI provides an explained situation instead of a raw detection, control room operators learn from context and repeated patterns. This assists in building reliable standard operating procedures. Finally, automated prioritisation helps leaders track KPIs and justify investments. The result is measurable gains in operational performance and long-term cost savings.
AI vision within minutes?
With our no-code platform you can just focus on your data, we’ll do the rest
comparing ai systems in genetec and milestone
Comparing AI systems across Genetec and Milestone requires a clear checklist. Start with ease of integration. Milestone’s open platform and SDK make third-party modules straightforward to deploy. Genetec offers a unified Security Center that centralises data and control. Each approach has trade-offs for scalability, hardware needs, and vendor support.
Scalability depends on the chosen architecture. On-prem AI models require GPU capacity when you handle many video streams. Cloud options reduce local hardware strain but raise questions about sending video to the cloud and data governance. For organisations with strict information security requirements, on-site processing with responsibly sourced data is often preferable. visionplatform.ai focuses on on-prem VLMs and agent architectures that maintain full control and reduce exposure.
Compatibility with existing cameras matters too. Most modern ONVIF or RTSP cameras integrate with both VMS brands, but high frame rates and analytics overlays increase bandwidth. Plan your network and storage around enriched metadata and longer retention of tagged events. If you need to search across cameras and timelines using natural queries, confirm that the chosen agent suite exposes structured events and that it provides reliable indexing. This capability provides structured access to events and helps operators to search without hunting through raw footage.
On privacy and compliance, follow GDPR best practices and maintain auditable logs. Responsible AI means using models whose training data is documented and whose reliability are essential for fair outcomes. Choose vendors that support model updates and offer transparency about how models were trained. Finally, consider whether you want an agent suite that can integrate with access control systems and business processes. That integration allows teams to correlate badge reads, alarms, and video intelligence to make faster, more accurate decisions.
ai-powered optimisation and integration best practices
Successful AI deployments hinge on model training, calibration and ongoing tuning. Start with a pilot that reflects site conditions. Gather representative video samples and train models to handle lighting, camera angles, and local behaviours. When you fine-tune models with local data, false positives fall and accuracy rises. Schedule periodic re-evaluation because environments change and models degrade if left static.
Vendor support and lifecycle management are also critical. Ensure your provider offers scheduled updates and clear rollback paths. The VP Agent Suite approach illustrates how agents can be versioned and audited. That reduces risk and simplifies maintenance. For long-term ROI tracking, instrument your control room to measure reductions in manual review time, lowered dispatches, and improved response times. These KPIs show business value and help prioritise next steps.
For integration, expose event streams through APIs, webhooks, or MQTT so connected systems can consume them. The agent suite addresses this challenge by providing structured event outputs and action interfaces. Also implement clear permissioning so that automated actions respect operational policies and human oversight. Responsible ai practices require configurable audit trails and the ability to maintain full control over sensitive video and metadata.
Finally, make sure you can scale from pilot to enterprise. Use modular deployments, and confirm hardware compatibility with your chosen AI models. visionplatform.ai supports GPU and edge options so you can deploy on servers or devices like NVIDIA Jetson machines. By following these practices you streamline rollout, reduce surprises, and ensure your investments drive measurable improvements across control room operations and business processes.
FAQ
What are AI agents in video surveillance?
AI agents are software components that reason over video events and take or recommend actions based on rules and context. They go beyond detection to provide verification, context, and suggested steps for control room operators.
How do AI agents work with Genetec Security Center?
AI agents integrate with Security Center via APIs or supported plugin frameworks and use event streams to augment detections with context. They can correlate video with access control events to reduce false positives and speed up verification.
Can Milestone XProtect support agentic AI?
Yes, XProtect supports third-party modules and SDKs that let vendors deploy agentic AI that reasons and acts on top of video streams. The agent suite for Milestone XProtect can expose device information through the Milestone API for richer analysis.
Do AI systems send video to the cloud?
Not necessarily. Many deployments keep processing on-prem and avoid sending video to the cloud to meet compliance and information security needs. visionplatform.ai offers on-prem options that process video and run the vision language model locally.
How much can AI reduce manual review?
Reported reductions vary by deployment, but case studies show substantial savings; one test with coram.ai reported a 30% reduction in manual review time. Results depend on model fit and operational tuning.
What are common use cases for AI-driven surveillance?
Use cases include perimeter breach detection, people-counting, vehicle detection and classification, loitering detection, and object-left-behind detection. These help teams find incidents faster and improve situational awareness.
How does a vision language model help operators?
A vision language model converts video frames into human-readable descriptions so operators can search across cameras and timelines using natural language. This simplifies forensic search and helps operators to search for events without exact timestamps.
What privacy safeguards should be considered?
Implement on-prem processing when possible, maintain auditable logs, and use responsibly sourced data for model training. Ensure compliance with GDPR and related regulations and configure retention and access controls carefully.
How do I measure ROI for AI deployments?
Track KPIs such as reduced false alarms, decreases in manual review time, faster response times, and fewer unnecessary dispatches. These metrics demonstrate operational improvements and help justify further investment.
Where can I learn more about specific AI features for airports?
For focused examples, check pages like people-counting, forensic search, and perimeter breach detection to see how AI addresses airport scenarios and to explore technical details and deployments. For instance, see our detailed forensic search in airports and people-counting in airports pages for applied examples.
