How does AI distinguish between real threats and false alarms?

AI distinguishes between real threats and false alarms using several advanced techniques:

Object Detection & Classification: AI analyses video streams to detect and classify objects like people, vehicles, or animals. This helps filter out irrelevant motion (e.g., trees moving in the wind) that would otherwise trigger false alarms.

Behaviour Analysis: AI uses predefined rules and machine learning to recognise unusual behaviour patterns, such as loitering or unauthorised access, and differentiate them from everyday activities.

Scene Noise Canceling: Advanced AI models apply scene noise-cancelling algorithms to filter out false triggers caused by environmental factors, such as moving shadows, changing light conditions, or animals.

Continuous Learning & Cloud-Based AI: Cloud-based AI solutions continuously update and improve their models, reducing false positives over time without requiring manual firmware updates.

Verification by AI & Human Operators: AI verifies alarms before sending them to monitoring stations. Operators then review AI-verified alerts, further reducing false alarms before responding.

AI-Driven Filtering Systems: Use deep learning to remove over 90% of false alarms by recognising movement patterns that do not correspond to real threats, such as reflections, rain, or animals.

By leveraging these technologies, AI significantly improves security efficiency by minimising unnecessary operator workload and enhancing real-time threat detection.