What types of AI technologies are used in AI Video Monitoring?

AI Video Monitoring incorporates several advanced artificial intelligence technologies to enhance security, reduce false alarms, and improve efficiency. The key technologies used include:

AI Video Analytics: Machine learning and deep learning algorithms analyse video to detect, classify, and track objects or events within a scene. This allows for automated video analysis beyond simple motion detection.

Object Detection and Classification: AI-powered object detection identifies and classifies objects like people, vehicles, and animals. AI-powered object detection identifies and classifies objects like people, vehicles, and animals. Convolutional Neural Networks (CNNs) like YOLO (You Only Look Once) and Faster R-CNN are often used.

False Alarm Filtering: AI filters out false alarms caused by environmental factors such as reflections, moving leaves, animals, and weather conditions. This significantly reduces the number of false positives operators need to handle.

Behavioural Analysis: AI is used to recognise and interpret human behaviour in video, identifying suspicious activities such as loitering, running, or other anomalies that may indicate security threats.

Scene Noise-Canceling: AI algorithms help eliminate scene noise, such as shadows and minor environmental changes, to prevent unnecessary false alarms.

Cloud-Based Video Analytics: Cloud-based AI processes video streams remotely, eliminating the need for local hardware and enabling continuous AI model updates for improved accuracy over time.

Real-Time Threat Detection: AI-powered systems continuously analyse video feeds, offering proactive monitoring to detect and prevent potential security threats before they escalate.

Perimeter Intrusion Detection: AI extends the range of video surveillance, using algorithms to detect and track potential threats over long distances. It integrates with PTZ cameras to zoom in and classify objects as people or vehicles.

Continuous AI Training and Improvement: AI models are continuously trained using real-world data to improve accuracy in detecting true threats and reducing false positives over time.

These AI-driven capabilities significantly enhance video surveillance operations by automating threat detection, reducing operator fatigue, and providing valuable security insights.