COMPUTER VISION AND FIELD INTELLIGENCE
Camera data is useful only when the observation leads to a governed action.
Computer-vision projects are framed around a narrow event, operating environment and response workflow. Lighting, camera position, privacy, false alerts and the cost of a missed event are assessed before any model is selected.
01Define the operational event
The project starts with a precise description of what must be observed and why.
- Object, condition or sequence to detect.
- Where and under which environmental conditions it occurs.
- Who reviews the signal and what action follows.
02How the pilot works
Representative footage and edge cases are evaluated with process owners.
- Assess image quality, coverage and privacy constraints.
- Label a bounded sample and establish a baseline.
- Test thresholds, human review and alert routing in context.
03What the organisation receives
The deliverables cover the full operating loop.
- Event definition and data-readiness report.
- Pilot evaluation with false-positive and false-negative analysis.
- Integration, review and escalation design.
Acceptance boundaryA vision model is not judged by a single accuracy number. Measures must reflect the real process: missed-event cost, false-alert burden, review time, environmental variation and whether the resulting action can be audited.
Field conditions define the result
A representative evaluation plan records camera position, distance, movement, weather, lighting and any seasonal or operational variation. The same threshold can produce very different consequences when a missed event is costly or when repeated false alerts interrupt teams. Pilot review therefore uses event-level examples and an agreed response procedure, not a headline accuracy figure. Privacy zones, retention, access and signage requirements remain subject to the organisation’s legal and security approval.