Running Nx Witness in a virtualised environment has both advantages and considerations regarding security. On the positive side, virtualisation allows for quicker deployment of security updates and patches, centralised management, and more accessible backup and recovery processes. It can also make it easier to isolate different applications, potentially reducing the impact of a security breach […]

Integration with other third-party systems including alarm & access control systems: API: Using the Server API, developers can access nearly every feature available in the system’s thick client, including but not limited to: Query/manage system resources (servers, cameras, users). Manage users and permissions. Pull live or recorded video out of the system. Generate events. Create […]

While Smart Motion technology in Nx Witness offers significant benefits, it’s essential to consider its potential limitations. One such limitation is its reliance on camera quality and positioning. If the cameras are of low resolution or are not correctly positioned, the Smart Motion detection may produce false positives or miss relevant events, impacting its effectiveness. […]

Smart Motion technology in Nx Witness significantly enhances the efficiency of video search by intelligently analysing and categorising motion events. Traditional searches may require users to manually sift through hours of video, but Smart Motion allows for quick and precise retrieval of relevant video clips. By focusing only on frames where motion occurs, the technology […]

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, […]