A 1080p camera that met identification-grade coverage across a 25-foot-wide doorway in 2024 now meets it across 12.6 feet. Nothing about the camera changed. The yardstick did. On 9 October 2025 the International Electrotechnical Commission published IEC 62676-4:2025, Edition 2.0 of the application guidelines for video surveillance systems, and it retired the four-level DORI scale that has governed security camera placement design for a decade.
The replacement is a seven-level scale, and the level that lets you establish who someone is now demands 500 pixels per meter instead of 250. If your coverage plan was drawn against the old numbers, roughly half of what you believed was identification-grade no longer is.
That sounds like bad news. For most facilities it is actually a clarifying one, because it puts a number on something security directors have suspected for years: the cameras watching your parking lots, fence lines and loading docks were never going to identify anyone, and designing them as though they might was the wrong goal all along.
Security camera placement is a pixel density problem, not a camera count problem
Placement quality is measured in pixel density: how many pixels the camera lays across one meter of the scene at the distance you care about. The arithmetic is unforgiving and simple. Divide the camera’s horizontal resolution by the width of the scene it covers, and you have your pixels per meter.
A 1920-pixel-wide sensor aimed across a 96-meter yard delivers 20 pixels per meter. Point the same camera at a 3.84-meter section of a doorway and it delivers 500. Same sensor, same night, a 25-fold difference in what the image can support. This is why counting cameras tells you almost nothing about coverage, and why a well-aimed 1080p camera routinely outperforms a 4K camera pointed at the horizon.
Two variables move that number: resolution and field of view. Resolution is fixed the day you buy. Field of view is set by lens choice and by where you put the camera, which means placement is the variable most facilities can still change without spending money on hardware.
The seven levels that replaced DORI
IEC 62676-4:2025 splits surveillance objectives into seven operational requirements instead of four, grouped into low and high pixel density objects. The old DORI ladder ran Detect (25 px/m), Observe (63 px/m), Recognize (125 px/m) and Identify (250 px/m). The new ladder runs Overview (20), Outline (40), Discern (80), Perceive (125), Characterize (250), Validate (500) and Scrutinize (1500).
The revision was not arbitrary. As Axis Communications notes in its published guidance on the 2025 pixel density levels, the update accounts for higher-resolution digital IP cameras while considering factors like compression and noise. In other words, the 2014 numbers were derived in a world of less aggressive compression, and real-world footage at 250 px/m was not delivering what the standard promised. The committee raised the bar to match what actually holds up in evidence.
How far your cameras actually reach
The practical output of any placement exercise is a maximum scene width per camera, per objective. Below is that arithmetic worked out for three common sensor resolutions. Find your camera’s horizontal pixel count, find the objective you need, and you have the widest scene that camera can cover and still hit the requirement.
| Objective (px/m) | 1080p 1,920 px wide |
5MP 2,592 px wide |
4K 3,840 px wide |
|---|---|---|---|
| Overview (20) | 315 ft | 425 ft | 630 ft |
| Outline (40) | 157 ft | 213 ft | 315 ft |
| Discern (80) | 79 ft | 106 ft | 157 ft |
| Perceive (125) | 50 ft | 68 ft | 101 ft |
| Characterize (250) | 25 ft | 34 ft | 50 ft |
| Validate (500) | 12.6 ft | 17 ft | 25 ft |
| Scrutinize (1500) | 4.2 ft | 5.7 ft | 8.4 ft |
Figures are arithmetic: horizontal pixels divided by the pixel density requirement, converted to feet. They describe the geometric ceiling only. Optics quality, compression, motion blur and lighting all pull real performance below it.
Read the highlighted row carefully, because it is the one that reframes most coverage plans. A 4K camera can support identity-grade imagery across 25 feet. That is a doorway, a turnstile, a single register lane. It is not a lobby, a platform, a parking row or a fence line. Any camera asked to watch a space wider than that is doing a different job, whether or not the design documents say so.
Detection-grade placement is a different problem from identification-grade placement
Knowing that something is happening requires a fraction of the pixels that proving who did it requires. That gap is the entire reason a camera can be useful in real time and useless in court, and the 2025 standard widened it. Under DORI the spread between the lowest rung and identification was tenfold. Under the new scale it is twenty-five-fold.
AI video analytics live at the bottom of that ladder. Determining that a person is standing at a fence line at 2 a.m., that a vehicle has entered a restricted yard, that a crowd is forming at an exit, or that someone has fallen and not gotten up, are all low pixel density tasks. They ask what is happening in the frame, not whose face is in it. Distinguishing a weapon-shaped object or a fall takes more detail than simply spotting a person, but it remains far below the 500 px/m the standard now sets for establishing identity.
This is not a workaround. It is the design. IntelliSee does not use facial recognition, does not store or retain video, and does not attempt to establish identity. The system evaluates behavior and objects in the frame and sends an alert within seconds. That makes the placement question a far easier one to answer, because detection-grade coverage is a target your existing cameras can usually already hit.
The practical consequence: stop trying to make every camera an identification camera. Pick the handful of chokepoints where identity genuinely matters and engineer those to Validate grade. Everywhere else, accept that the camera’s job is to notice, and put detection intelligence behind it. A fence line at 40 px/m will never tell you who someone was. It can absolutely tell you, in real time, that someone is there.
Six placement decisions that matter more than adding cameras
Most coverage gaps are aim and lens problems rather than budget problems. These are the decisions that move pixel density most, in the order they should be made.
- Decide the question each camera answers before you choose a spot. Every camera should have one written objective: notice intrusion at the north fence, or support identity at the employee entrance. A camera with two objectives is usually failing at both, because the field of view that satisfies one will not satisfy the other.
- Measure scene width, not distance to target. Pixel density is set by how wide the frame is at the target plane, not by how far away the camera sits. Two cameras at the same distance with different lenses produce completely different results. Distance is the input people quote; scene width is the number that governs.
- Reserve identity-grade coverage for chokepoints. At 500 px/m you are covering roughly a doorway. Doors, gates, turnstiles, teller windows and register lanes are where that investment pays. Open ground never will, at any resolution you can currently buy.
- Mount for the target plane, not for the ceiling. Height that puts the camera out of reach also flattens the subject and compresses usable detail into fewer vertical pixels. Steep downward angles are good for counting and tracking movement, poor for anything that needs to see a subject face-on.
- Aim across the path of travel, not down it. A subject walking directly toward or away from the camera spends most of the sequence outside your intended density band. Framing across the path holds the subject at a consistent distance, which holds pixel density steady through the whole event.
- Re-verify after anything changes. Landscaping grows, construction reroutes traffic, and mounts drift out of alignment over months. A design validated once and never rechecked is a design that expires quietly. Cameras that go offline or drift without anyone noticing are the most common failure in otherwise well-planned systems.
What placement cannot fix
Pixel density is a ceiling on image quality, never a guarantee of it. A camera can satisfy every geometric requirement in the standard and still deliver an unusable image, and placement work should be paired with an honest look at the conditions the camera actually operates in.
Lighting is the first constraint. A scene that meets Discern grade at noon may fall well short at 3 a.m. under sodium lighting or in driving rain, which is why detection performance in low light, fog and rain deserves its own line in any coverage review. Compression is the second. Aggressive bitrate limits destroy detail that the geometry preserved, a tradeoff that shows up sharply in cloud video surveillance architectures where upstream bandwidth is the binding constraint.
The third constraint is the one no standard addresses. A perfectly placed camera producing a perfectly compliant image still does nothing on its own. Unmonitored cameras do not prevent incidents, they record them. The coverage plan determines whether an event is visible. Something still has to be watching for it, which is why AI perimeter detection has become the layer facilities add once they accept that staffing a monitor around the clock is not realistic.
Frequently asked questions about security camera placement
What is the correct mounting height for a commercial security camera?
There is no single correct height, and any guide quoting one without naming a target distance is guessing. Height is a tradeoff between tamper resistance and viewing angle: higher mounts protect the hardware and widen coverage but flatten subjects, reducing the usable detail for anything that needs a face-on view. Set the objective and target distance first, then choose the height and lens that deliver the required pixel density at that point.
Does IEC 62676-4:2025 apply in the United States?
It is an international standard rather than US law, so it carries no direct legal force in the United States. It matters because it is the reference specifiers, integrators and camera manufacturers design against, and because manufacturer coverage claims are increasingly stated in its terms. In Europe it is adopted as EN IEC 62676-4. Treat it as the technical benchmark your vendors are already using, not as a compliance obligation.
How many pixels per meter does AI video analytics need?
Less than identification requires, and the exact figure depends on what is being detected. Establishing that a person or vehicle is present sits at the bottom of the scale, while distinguishing specific objects or behaviors needs more detail. All of it sits well below the 500 px/m the 2025 standard now sets for establishing identity, which is why analytics frequently work on cameras that would fail an evidentiary review.
Do I need to replace my cameras to meet the new standard?
In most cases, no. The standard is a design reference, not a mandate, and the cheapest fixes are almost always re-aiming, changing a lens, or narrowing a field of view that was set too wide during installation. Replacement makes sense only where a chokepoint genuinely requires identity-grade imagery and no lens or position on the existing camera can deliver it.
What changed between DORI and the 2025 levels?
Four levels became seven, and the identification requirement doubled. DORI ran Detect, Observe, Recognize and Identify, with Identify at 250 px/m. The 2025 scale runs Overview, Outline, Discern, Perceive, Characterize, Validate and Scrutinize, and identity now sits at Validate, 500 px/m. The former Identify value of 250 px/m is now Characterize, a level that supports description rather than identification.
Placement sets the ceiling. Detection decides whether it matters.
The 2025 revision is a useful reality check. It states plainly, in numbers a facility manager can check with a calculator, that most cameras on most sites are not identification devices and were never going to be. The productive response is not a rip-and-replace cycle chasing pixel counts across open ground. It is to place a few cameras deliberately where identity matters, and to make every other camera capable of noticing something in time for someone to act.
That second half is the work IntelliSee does. The platform layers onto the cameras already installed, with no hardware replacement, and turns passive coverage into proactive protection: unauthorized access, perimeter breaches, loitering, weapons, falls, crowds, vehicles and smoke or fire, flagged in real time and routed to the people who can respond.
If you are reworking a coverage plan against the new standard and want a clear-eyed read on what your existing cameras can and cannot support, start a conversation with our team.
