Security Video Analytics

Intrusion detection AI uses computer vision models trained on millions of security scenarios. The system defines virtual boundaries (tripwires, restricted zones) around sensitive areas. When a person, vehicle, or object crosses these boundaries, the AI verifies the threat by analyzing object classification, trajectory, and behavioral context before triggering an alert. This multi-step verification reduces false alarms by 85% compared to simple motion detection.

Yes. AI-based security video analytics reduces false alarms by 80-90% compared to traditional motion-based systems. The AI distinguishes humans from animals, vehicles from shadows, and genuine threats from environmental triggers like rain, wind, or tree movement. Only verified threats generate alerts, eliminating the alert fatigue that plagues conventional CCTV systems.

AI security camera analytics can detect a wide range of threats including: perimeter intrusions, unauthorized access, loitering in restricted areas, abandoned objects, fire and smoke, crowd density anomalies, vehicle violations, tailgating through access points, vandalism, and theft. Each detection model is purpose-built and continuously refined with new training data.

Perimeter security AI creates virtual fence lines along physical boundaries using camera feeds. The AI continuously monitors these boundaries using object detection and tracking algorithms. When a human or vehicle approaches or crosses the perimeter, the system classifies the object, verifies the threat level, and triggers graduated alerts based on proximity and threat assessment. Unlike physical sensors, AI perimeter detection covers larger areas with fewer cameras and adapts to changing conditions.

Yes. Agrex security video analytics is camera-agnostic and works with any IP camera, analog camera connected via DVR, or existing NVR system. The AI processes video feeds via standard protocols (RTSP, ONVIF, HTTP) without requiring camera replacement. This means you can add AI-powered threat detection to your existing CCTV infrastructure within days, with zero hardware investment.

Organizations deploying AI security analytics typically see ROI within 3-6 months through reduced security staffing costs (30-50% fewer operators needed), lower false alarm response costs, faster incident resolution, and reduced theft/vandalism losses. A typical 200-camera deployment saves $150,000-$300,000 annually compared to manual monitoring while improving detection rates from 30% to over 90%.

Facial recognition works as one layer within the broader security video analytics platform. It compares faces detected in camera feeds against watchlists (authorized personnel, VIPs, persons of interest) in real-time. Combined with behavioral analytics, the system can identify unauthorized individuals, detect tailgating, and verify access control compliance. All facial recognition processing follows data privacy regulations including GDPR requirements for consent and data retention.

Yes, when properly implemented. Agrex security video analytics includes privacy-by-design features including automatic face blurring for non-security feeds, data retention policies that auto-delete footage after configurable periods, role-based access controls, and detailed audit logs. For GDPR compliance, organizations must display surveillance notices, maintain a register of processing activities, and conduct a Data Protection Impact Assessment (DPIA) for facial recognition deployments.

Security video analytics benefits any organization with physical security requirements. The highest-impact industries include: critical infrastructure (power plants, data centers), banking and financial services (ATM monitoring, branch security), retail (loss prevention, store security), logistics and warehousing, education campuses, healthcare facilities, smart cities, and government buildings. Each industry uses specialized AI models tuned for their specific threat profiles and compliance requirements.