Camera Coverage Zone Planning — A Practical Guide

A security or CV camera system is only as good as the spatial configuration behind it. Placing cameras without planning zone coverage leads to blind spots, overlapping detection areas that generate duplicate alerts, and privacy violations from cameras that inadvertently capture protected spaces.

This guide covers the key concepts of camera zone planning and how to document coverage areas in a format your system can use.

The four zone types you need

Field-of-view polygons — The actual area a camera can see, accounting for lens angle, mounting height, and obstructions. Document these to verify coverage and identify blind spots before finalising camera placement.

Detection zones — The subset of the camera’s field of view where you want detections to trigger. Often narrower than the full FoV — you might watch only the doorway, not the entire lobby visible to the camera.

Exclusion zones — Areas within the camera’s view that should be suppressed. Common examples:

A window showing a public street behind the monitored space (prevents traffic triggering alarms)
A neighbouring property visible through a fence
A private space (changing area, medical room) visible at the edge of the frame

Tripwires — Virtual counting lines placed where objects cross a boundary. A single line for presence detection; two parallel lines to determine direction of travel.

Drawing coverage polygons on a floor plan

Working on a floor plan (rather than on individual camera frames) lets you reason about spatial coverage holistically. You can see all cameras’ fields of view simultaneously, check for gaps, and verify that monitored zones actually overlap with the areas you care about.

Open RegionKit and load your floor plan image. Create layers for each zone type:

FoV (blue, low opacity) — field-of-view polygons, one per camera
Detection (indigo) — active monitoring zones
Exclusion (red, dashed) — suppressed areas
Tripwires (green) — counting lines

For each camera, draw its field-of-view polygon using the Polygon tool (P). A typical outdoor camera FoV is a wedge shape — narrow at the camera position, widening toward the edge of range. An indoor ceiling-mount camera typically has a circular or wide rectangular coverage area.

Label each polygon with the camera ID and add any relevant metadata tags (mounting height, lens type, etc.).

Calculating coverage gaps

Once all FoV polygons are drawn, toggle off the Detection and Exclusion layers to see only the camera coverage. Areas not covered by any FoV polygon are blind spots.

Common blind spot causes:

Walls and pillars — use the floor plan’s structural elements to predict obstructions
Camera angle — a camera mounted too high or angled too steeply may miss low-to-ground objects
Range limitations — optical and IR range limits create dead zones beyond the camera’s effective reach

For each blind spot, either reposition an existing camera or add a new one. Re-draw the FoV polygon after any camera position change.

Handling overlapping zones

Some overlap between adjacent cameras is desirable for redundancy. Too much overlap wastes coverage budget and can generate duplicate detections if your system doesn’t deduplicate across cameras.

In RegionKit, toggle the FoV layer on and set all polygons to a semi-transparent fill. Overlap regions appear darker — the more cameras covering an area, the more opaque it appears.

A rule of thumb for security systems:

Critical entry/exit points — 2× camera overlap is appropriate
General corridors — 1–1.5× overlap
Open areas — minimise overlap to reduce duplicate detection events

Privacy exclusion zones

Privacy-by-design for CV systems requires explicit exclusion zones for areas where recording would be unlawful or inappropriate. These are not suggestions — in many jurisdictions they are legal requirements.

Common privacy exclusion scenarios:

Adjacent properties — a camera covering a car park may capture a neighbouring property’s windows
Public rights of way — recording passers-by on a public footpath often requires a privacy impact assessment or is outright prohibited
Private spaces — medical consultation rooms, changing areas, prayer rooms visible at the edge of a camera’s FoV

Draw these as Exclusion Zone polygons in RegionKit, export them with your configuration, and filter detections against them in your inference code:

# Any detection whose centroid falls inside an exclusion zone is suppressed
import cv2, numpy as np

def is_excluded(cx, cy, exclusion_zones):
    return any(
        cv2.pointPolygonTest(zone, (float(cx), float(cy)), False) >= 0
        for zone in exclusion_zones
    )

Documenting coverage for audits

Camera zone documentation is required for security audits, insurance surveys, and regulatory compliance in many industries. A well-structured RegionKit scene provides this documentation automatically:

Export as PNG — a flat composite of the floor plan with all zone polygons renders in a single image suitable for reports
Publish to URL — share a link for remote review; no file transfer needed
Version history — each publish creates a new version; reviewers can compare zone configurations over time to verify changes were authorised

Label each zone clearly with the camera ID and zone type. Include tags for relevant metadata — installation date, maintenance schedule, review date — so the exported JSON functions as a living configuration document.