Demonstrates privacy-aware visual analytics for operational safety, crowd flow, access anomalies and incident triage using demo demo frames.
The examples below show where this project can be used, what decision it supports, and how a user can test the scenario in the sandbox.
Where it fits: Detect missing safety equipment, restricted-zone entry or unsafe proximity patterns in operational areas.
Decision supported: Prioritise safety intervention and reduce missed hazards.
How to test it: Use the demo frames and review event categories.
Where it fits: Flag unexpected movement patterns in restricted areas or after-hours access events.
Decision supported: Support faster triage by facility/security teams with human review.
How to test it: Inspect event timeline and alert priority.
Where it fits: Monitor crowd build-up, direction changes or bottlenecks at entrances, exits and queues.
Decision supported: Improve crowd management and reduce operational risk.
How to test it: Review heatmap-style or event-summary visuals.
Where it fits: Track movement around loading bays, high-value assets or restricted zones.
Decision supported: Improve asset visibility and incident reconstruction.
How to test it: Use the action queue to see event owner and recommended follow-up.
Where it fits: Monitor queue pressure, waiting-area density and service bottlenecks.
Decision supported: Support staffing and patient-flow decisions without identifying individuals.
How to test it: Use aggregate-count outputs, not identity-based analytics.
Where it fits: Use demo event-level signals with review workflows, retention controls and bias checks.
Decision supported: Avoid over-reliance on automated decisions and reduce privacy risks.
How to test it: Review methodology and governance notes before applying to real environments.
The examples above were revised against current industry and research references on how this class of analytics solution is used in practice.