AI Video Monitoring can significantly reduce false alarms but cannot eliminate them. How AI Video Monitoring Reduces False Alarms: AI-Based Filtering: AI Video Monitoring uses AI video analytics to filter out “False Positive” alarms by distinguishing between genuine threats and benign triggers like moving trees, animals, or changing light conditions. Advanced Object Classification: AI video […]

Using video analytics, including object detection, classification, and behaviour analysis, AI differentiates between human movement and environmental factors like rain, wind, or animals. Here’s how it works: Object Detection & Classification: AI-powered surveillance systems use deep learning models to identify and classify objects in video. These models, trained on extensive datasets, can distinguish between humans, […]

AI handles occlusions—such as an object blocking the camera’s view—by using various techniques within video analytics and artificial intelligence. Here’s how AI-based video monitoring systems mitigate occlusion challenges: Object Persistence and Tracking: AI-based video analytics use object tracking algorithms to predict and maintain the location of partially or temporarily occluded objects. By analysing an object’s […]

AI improves over time through continuous learning, primarily via the following mechanisms: Data-driven learning and Model Training AI systems continuously ingest new data, allowing their models to learn and adapt. The more data they process, the better they become at identifying patterns, making predictions, and reducing errors. AI video analytics, for instance, use machine learning […]

A false positive in AI video analytics occurs when the AI incorrectly identifies an event, object, or action as a security threat when no actual threat exists. The system generates an alarm based on this incorrect interpretation of video data. For example, a false positive might occur when: The AI mistakenly classifies a shadow, moving […]

AI Video Monitoring is highly effective in detecting threats but is not infallible. Its accuracy depends on various factors, including environmental conditions, system setup, and AI model training. Here are some key insights from different AI-powered video monitoring solutions: Accuracy of Threat Detection: AI Video Monitoring uses object detection and classification to differentiate between humans, […]

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 […]

Object detection in AI Video Monitoring is a computer vision technique for identifying and locating objects within an image or video. It involves classifying objects into predefined categories and determining their positions, allowing for the recognition and spatial understanding of various objects in a scene. Key Aspects of Object Detection: Classification and Localisation: Object detection […]

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. […]

How can the combination of AI video surveillance and VMS enhance perimeter security? AI-powered video surveillance and Video Management System (VMS) integrations can significantly enhance perimeter security by improving detection accuracy, reducing false alarms, and enabling real-time threat response. Here are key ways they contribute to better perimeter security: Advanced Threat Detection & Reduced False […]