Automates the detection of potential incidents from video using trained machine-learning models, enabling continuous monitoring, faster alerts and reduced reliance on human operators.

Manual monitoring of surveillance video is labour-intensive and may delay the identification of physical incidents or public-safety risks, particularly when large volumes of moving-image data need to be continuously monitored.

Provides an AI-based video-analysis system that processes multiple image frames using a trained prediction model to identify incidents such as fights or quarrels and automatically generate alerts when a potential incident is detected.

Applies a trained machine-learning prediction model directly to moving-image frames to automatically recognise potential physical incidents and trigger alerts, reducing dependence on continuous manual video surveillance.