SafeVision
Computer-vision safety monitoring.
Overview
SafeVision applies real-time computer vision to camera feeds to detect safety hazards and policy violations, alerting teams the moment risk is detected.
The challenge
Safety monitoring that relies on people watching camera feeds does not scale. Attention drifts, coverage is inconsistent across shifts and sites, and hazards or policy violations are often noticed only when reviewing footage after an incident has already occurred — when it is too late to prevent it.
Our approach
SafeVision analyses live camera feeds in real time. Computer-vision models built with OpenCV and PyTorch detect the objects and events that matter, and each site can define its own alert rules and monitored zones so the system flags only genuinely relevant risks rather than drowning operators in false positives. A Django backend records every detection and serves an incident dashboard with full history for review and reporting, and the entire stack is containerised with Docker so it can be deployed close to the cameras — including on edge hardware — without a heavyweight cloud dependency.
Key features
- Real-time object & event detection
- Configurable alert rules and zones
- Incident dashboard with history
- Edge-friendly, containerised deployment
Outcome
SafeVision demonstrates a shift from reactive to proactive safety monitoring: instead of reviewing footage after the fact, teams are alerted the moment a hazard appears. It shows how modern computer vision can turn existing camera infrastructure into an always-on safety layer without replacing the hardware already in place.
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