How do I calculate how many pixels I need at a doorway for facial identification quality?

IEC 62676-4 defines Identify level as 250 pixels per meter at the subject. An average adult face is approximately 0.2 meters wide, so you need 250 * 0.2 = 50 horizontal pixels across the face. A 1920-pixel-wide frame covering a 2-meter-wide doorway delivers 1920/2 = 960 ppm — well above the 250 ppm Identify threshold. The constraint becomes apparent when the camera is positioned to cover a wider area: a 1920p camera covering an 8-meter-wide lobby entrance delivers only 240 ppm, just below Identify threshold. The solution is either a dedicated face-capture camera aimed tightly at the 1–2 meter wide doorway, or a higher-resolution (4K = 3840 horizontal pixels) camera that can cover the wider area while maintaining 250+ ppm.

What focal length should I use for a camera covering a parking lot entrance at 15 meters?

Work backward from your pixel density requirement. For vehicle overview (Observe level, 62 ppm) of a 6-meter-wide entrance lane at 15 meters with a 1/2.8-inch sensor 1080p camera: required coverage width = 6 m, delivering 1920/6 = 320 ppm — Identify level, more than sufficient. The focal length for 6 m coverage at 15 m: using a 1/2.8-inch sensor (5.4 mm width), required FoV = 2*arctan(3/15) = 22.6°, which corresponds to approximately a 13–14 mm focal length lens. For license plate capture you want a separate dedicated LPR camera with a longer focal length (25–50 mm), higher shutter speed, and IR illumination aimed along the lane rather than across it.

Is a single 4K fisheye camera a cost-effective replacement for multiple conventional cameras in a large open room?

For Detect and Observe level requirements in an open room without obstructions, yes — a single 8MP fisheye camera at ceiling center can provide panoramic coverage of rooms up to 15x15 meters with acceptable pixel density for scene monitoring and general tracking. The critical limitation is pixel density at the room perimeter: in a 12-meter diameter room, the dewarped image from an 8MP fisheye camera delivers approximately 60–80 ppm at 6 meters from the camera — marginal for Observe, insufficient for Recognize or Identify. Do not use fisheye cameras as the sole coverage for positions where facial identification may be required. The cost-effective hybrid design uses one fisheye for room overview context plus two to four fixed cameras at entry/exit points for Identify-level forensic coverage.

What is variable bitrate (VBR) and how much storage does it actually save?

Variable bitrate (VBR) encoding adjusts the compression ratio frame-by-frame based on scene complexity. A static parking lot at 3 AM with no movement can be compressed at 0.5–1 Mbps (high compression ratio on redundant background frames) without visible quality loss. The same camera during a busy daytime scene with vehicle movement encodes at 4–8 Mbps to preserve motion detail. VBR with H.265 and motion-triggered quality boost (sometimes called "smart codec" or "smart stream" by manufacturers) typically reduces average bitrate by 40–70% compared to constant bitrate (CBR) settings in typical security applications with mixed busy/quiet periods. Over a 30-day retention period, this translates directly into proportional storage reduction — a system designed for 130 TB CBR may require only 50–70 TB in practice with VBR enabled.

How much camera field of view overlap is needed between adjacent cameras to avoid coverage gaps?

Best practice is 15–20% horizontal FoV overlap between adjacent cameras at the maximum required DORI coverage distance. This means at the far edge of each camera's specified coverage range, the adjacent camera has already picked up the scene with 15–20% of its own frame. Below 10% overlap, a subject moving between cameras will have a brief coverage gap during which their position and actions are unrecorded — a gap that defense attorneys regularly exploit in prosecutions. Above 30% overlap, you are over-specifying camera density and adding unnecessary hardware cost. When designing layouts in JVSG or similar tools, set your FoV footprints to overlap by 15–20% at the specified DORI range, not at the camera's maximum physical detection distance.

Security Camera Layout and Coverage Analysis: Field of View, Mounting Height, and Lens Selection

Engineering methodology for designing security camera coverage — calculating field of view, selecting focal lengths, determining mounting heights, performing coverage gap analysis, and documenting camera layouts to IEC 62676-4 pixel density requirements.

The DORI Framework: Detect, Observe, Recognize, Identify

IEC 62676-4 (Video Surveillance Systems for Use in Security Applications — Part 4: Application Guidelines) defines the DORI framework as the standard methodology for specifying camera performance requirements. DORI stands for four image quality levels, each defined by a minimum pixel density at the subject:

Detect (D) — 25 pixels per meter (ppm). The minimum level for detecting that a human or vehicle is present in the scene. Adequate for open area monitoring and parking lot overview.
Observe (O) — 62 ppm. Sufficient to observe general clothing, body posture, and movements of a subject. Used for corridor and lobby overview cameras.
Recognize (R) — 125 ppm. Enables confirmation that an observed person matches a known individual from a reference image. Required for secondary exit/entry confirmation.
Identify (I) — 250 ppm. Forensic-quality imagery enabling reliable identification of an unknown subject from camera footage alone. Required at primary entry points, ATMs, cashier positions, and evidence-quality coverage zones.

Topics covered

security camera layoutCCTV field of viewcamera mounting heightlens focal length selectionIEC 62676-4pixel density CCTVcamera coverage analysisCCTV designfisheye camera securityvarifocal lens securityPPM pixels per meterDORI standardsecurity camera placement guidecamera coverage overlapNVR camera resolution