FOV calculator — camera field of view, IFOV & coverage

Field of view

Sensor1

Sensor width (mm)

Sensor height (mm)

Horizontal pixels

Vertical pixels

Pixel pitch (µm, for IFOV)

Focal length (mm)

Distance (m)

Coverage at distance

Scene coverage (degrees)

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Scene coverage (meters)

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IFOV (central)

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How this is calculated

Field of view. For each axis, the angle the sensor sees:

FOV = 2 · arctan( sensor dimension ÷ (2 · focal length) )

Scene coverage. How much real-world width, height and diagonal the frame spans at the set distance:

coverage = 2 · distance · tan( FOV ÷ 2 )

IFOV (central). The exact rectilinear angular width of one pixel at the centre of the field:

IFOV = 2 · arctan( pixel pitch ÷ (2 · focal length) )

Angular sampling. The average angular size per pixel across the whole image (FOV ÷ pixel count). This is close to, but not identical with, the central IFOV — it is an image-average, shown here in µrad/px.

Model limits. Rectilinear (pinhole) projection. Real lenses vary across the field and add their own MTF and chromatic effects — treat this as first-order geometry.

Engineering assumptions

Projection: Rectilinear · Lens distortion: Not modelled · Atmosphere: Not modelled / simplified

Optical MTF: Not modelled · Stabilization: Not modelled · Ground: Flat horizontal plane

1Sensor library. A curated convenience library — specs are unverified; check the datasheet before relying on a preset. NETD values are selected nominal assumptions ("typically < X mK" under stated conditions and variant), not guaranteed values; detector and module specifications can differ. "Format" rows are generic optical-format approximations (no specific part, no band), so they don't appear under the VIS / LWIR filters.

Field of view calculator for EO/IR cameras

This calculator works out the angular field of view (FOV) of a camera from its sensor size and lens focal length, then projects that angle onto a scene at a chosen distance to give real-world coverage width and height. It also returns the single-pixel angular sampling — both the central instantaneous field of view (IFOV) and the frame-average angular sampling — which together tell you how much ground each pixel sees.

It is a first-order geometric model intended for sizing a sensor-and-lens pairing early in a design: choosing a focal length for a required coverage, checking whether a given camera resolves enough detail at range, or comparing candidate sensors on a like-for-like basis. For a fixed sensor, a shorter focal length widens the field and coarsens angular sampling; a longer focal length narrows the field and samples the scene more finely.

How field of view is calculated

Angular FOV along any axis follows directly from the right-triangle geometry of a pinhole (rectilinear) lens:

FOV = 2 · arctan( sensor dimension ÷ (2 · focal length) ) — computed separately for the horizontal, vertical and diagonal sensor dimensions.
Scene coverage = 2 · distance · tan( FOV ÷ 2 ) — the real-world width or height captured at the target distance.
IFOV (central) = 2 · arctan( pixel pitch ÷ (2 · focal length) ) — the exact angular width of one pixel at the centre of the field.
Angular sampling = FOV ÷ pixel count — the average angle per pixel across the whole image, close to but not identical with the central IFOV.

Worked example

A 1/2.8″ sensor (5.57 × 3.13 mm, 1920 × 1080 px, 2.9 µm pitch) with a 50 mm lens, imaging at 1 km:

HFOV = 2·arctan(5.57 ÷ (2·50)) = 6.37° Scene width = 2·1000·tan(6.37°/2) = 111 m IFOV = 2·arctan(0.0029 ÷ (2·50)) = 58 µrad (≈ 5.8 cm at 1 km)

So this configuration frames a 111 m-wide scene at 1 km, with each pixel subtending about 5.8 cm on the ground — enough to detect a vehicle, but not to read markings on it.

Frequently asked questions

What is the difference between IFOV and angular sampling?

IFOV (instantaneous field of view) is the exact angular width of a single pixel at the centre of the lens, derived from pixel pitch and focal length. Angular sampling is the field of view divided by the pixel count — an image-average. For a rectilinear lens the two are nearly equal near the centre but diverge toward the edges, where the same pixel pitch subtends a slightly different angle. We report both so you can distinguish the on-axis best case from the whole-frame average.

How do I choose a focal length for a required coverage?

Enter your sensor and target distance, then adjust the focal length until the scene width matches your requirement. A wider required coverage needs a shorter focal length; to double the coverage at a fixed distance, roughly halve the focal length. The relationship is set by coverage = 2·distance·tan(FOV/2), with FOV itself driven by sensor size over focal length.

Does lens distortion affect these numbers?

This calculator uses an ideal rectilinear (pinhole) projection and does not apply lens distortion. Real wide-angle lenses show barrel distortion that slightly increases the true captured field at the edges, while telephoto lenses can show mild pincushion. For first-order sizing the rectilinear value is the right reference; for final optical design the lens's measured distortion profile must be applied.

Why are horizontal, vertical and diagonal FOV different?

Each is computed from a different sensor dimension. A 16:9 sensor is wider than it is tall, so its horizontal FOV exceeds its vertical FOV, and the diagonal — the longest dimension — gives the largest angle. Lens "FOV" specifications usually quote the diagonal unless stated otherwise, which is worth checking when comparing data sheets.

What sensor and lens data do I need?

Sensor width and height in millimetres, horizontal and vertical pixel counts, the lens focal length in millimetres, and the target distance. Pixel pitch is needed for the IFOV figure. Selecting a sensor from the built-in picker fills the sensor dimensions, resolution and pitch automatically; the lens focal length and distance are then the only values you enter.

Target sampling

Sensor1

Sensor width (mm)

Sensor height (mm)

Horizontal pixels

Vertical pixels

Target

Target width (m)

Target height (m)

Distance (m)

Focal length (mm)60 mm

Min Max

HFOV × VFOV

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Pixels on target (H×V)

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Scene (W×H)

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Pixel density

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Ground sample dist.

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Target angular size

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Target fills

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How this is calculated

Field & coverage. At the selected focal length, each axis uses FOV = 2·arctan(sensor ÷ (2·f)) and coverage = 2·distance·tan(FOV/2).

Pixels on target. How many pixels span the target on each axis:

pixels = ( target dimension × focal length ) ÷ ( range × pixel pitch )

This direct form is equivalent to target size ÷ ground sample distance, and uses the same geometry as the DRI tool. Both horizontal and vertical are shown on the preview.

Model limits. Rectilinear geometry only, assuming a front-facing target of the entered width and height. Oblique aspect angle, partial occlusion and target depth reduce the actual projected pixels. Resolvable detail also depends on optical MTF, focus accuracy, atmosphere and display, so pixels-on-target is a geometric ceiling, not resolvable detail.

Engineering assumptions

Projection: Rectilinear · Lens distortion: Not modelled · Atmosphere: Not modelled / simplified

Optical MTF: Not modelled · Stabilization: Not modelled · Ground: Flat horizontal plane

1Sensor library. A curated convenience library — specs are unverified; check the datasheet before relying on a preset. NETD values are selected nominal assumptions ("typically < X mK" under stated conditions and variant), not guaranteed values; detector and module specifications can differ. "Format" rows are generic optical-format approximations (no specific part, no band), so they don't appear under the VIS / LWIR filters.

Detection, Recognition, and Identification (DRI)

Camera type

Sensor1

Horizontal pixels

Vertical pixels

Pixel pitch (µm)

Focal length (mm)

Geometric probability assumption (×1.0)2

Target A

Target

Overall width (m)

Overall height (m)

Critical dimension (m)3

Compare a second target

Advanced pixel assumptions

Pixels across the critical dimension per task — a simplified Johnson geometric pixel-count model (≈1.5 / 6 / 12 at 50%). A first-order engineering estimate, not a universal DRI standard; the geometric probability assumption scales these values.

Detection (px)

Recognition (px)

Identification (px)

Environmental / Signal limit optional

Optional simplified signal-limited screening estimate from contrast, atmospheric attenuation and sensor sensitivity — not a validated DRI model. It computes a separate signal range for each task (detection, recognition, identification) and never changes the geometric pixels-on-target; the final range per task is the lower of its geometric and signal limits. The task thresholds are illustrative, editable assumptions, not calibrated performance limits.

Apply Environmental / Signal limit

Environmental / signal probability assumption (×1.0)5

Target–background ΔT (K)

Atmos. attenuation σ (km⁻¹)

Sensor NETD (mK)

Apply optional signal attenuation factor

Advanced signal assumptions

Required SNR per task — illustrative assumptions, not calibrated. Higher demands → shorter range. Uses |ΔT| so a cooler-than-background target still has contrast.

SNR — Detection

SNR — Recognition

SNR — Identification

Typical values

Atmos. attenuation σ (km⁻¹):
A user-supplied illustrative LWIR coefficient — thermal transmission depends on wavelength, humidity and weather, so it is not equivalent to visible-light meteorological visibility. Enter a value from your own band- and condition-specific data; the EO ranges (0.2 clear → ~0.8 fog) are only a rough starting point.

Target–background ΔT:
~2 K day vehicle vs background, ~0.5–1 K low-contrast, ~5–10 K hot engine/exhaust.

NETD:
Sets the thermal contrast floor only — not a complete system-sensitivity model (spatial, temporal, spectral and processing effects are not captured).

SNR:
A configurable first-order threshold, not a universal DRI criterion. ≈ 5 is commonly associated with a Rose-type detection criterion; lower values are a more permissive screening assumption. The defaults (2.5 / 4 / 6) are placeholders for demonstrating increasingly demanding tasks.

Signal attenuation factor:
An optional single scalar on contrast, not a computed MTF at a defined spatial frequency. ~0.7 well-focused, ~0.4–0.5 modest, ~0.2 near the resolution limit.

Estimated DRISimplified Johnson-style geometric estimate · 50% probability

Detection

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Geometric-limited

Recognition

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Geometric-limited

Identification

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Geometric-limited

Geometric sampling limitsimplified Johnson-style criterion

Task	Range
Detection	—
Recognition	—
Identification	—

Environmental / signal limitoptional

Enable the Environmental / Signal limit below to add a task-specific signal range.

Critical dim. (A / B)

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HFOV × VFOV

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IFOV

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How this is calculated

The geometric and signal limits are computed independently for each task, and the final per-task range is the lower of the two, so the result is auditable.

Geometric sampling limit (per task N = 1.5 / 6 / 12):

R = (critical dimension × focal length) ÷ (N × pixel pitch)

Environmental / signal limit — computed separately for each task. Thermal, using |ΔT| and the task's required SNR:

|ΔT| · e^−σR · MTF ≥ SNR_task × NETD ⟹ R = ln( |ΔT|·MTF ÷ (SNR_task·NETD) ) ÷ σ

Visible/EO uses the same form with initial contrast C and the task's minimum remaining contrast C_min,task: R = ln( C ÷ C_min,task ) ÷ σ. If the signal is already below threshold at zero range the task range is zero ("below signal threshold"); if σ = 0 and the threshold is met there is no atmospheric limit in the model. NETD and atmosphere affect these signal limits only — never the geometric pixels-on-target.

All DRI outputs are first-order estimates, not guaranteed real-world performance, and should not be interpreted as measured or validated system range. Actual range depends on target contrast, atmospheric transmission, optics MTF, sensor sensitivity, image processing, stabilization, display and observer/algorithm performance.

Engineering assumptions

Projection: Rectilinear · Lens distortion: Not modelled

Geometric model: No atmospheric degradation · Signal / environment model: Optional first-order screening

Optical MTF: Not modelled unless used in signal screening · Stabilization: Not modelled

1Sensor library. A curated convenience library — specs are unverified; check the datasheet before relying on a preset. NETD values are selected nominal assumptions ("typically < X mK" under stated conditions and variant), not guaranteed values; detector and module specifications can differ. "Format" rows are generic optical-format approximations (no specific part, no band), so they don't appear under the VIS / LWIR filters.
2Geometric probability assumption. Scales the geometric pixel requirement only — it does not change the signal model's contrast/SNR thresholds, which are independent illustrative assumptions. Actual discrimination probability depends on target characteristics, contrast, sensor performance, image processing and observer/task conditions; a calibrated probability claim would require validating the complete system model, not just a geometric multiplier.
3Critical dimension. A representative target dimension or feature relevant to the selected task. This value drives the geometric range calculation; overall width and height describe the target's scale but do not directly affect the calculated range. Choose the critical dimension based on the target, viewing angle and task; built-in values are preliminary engineering assumptions. V1 uses one representative critical dimension, while advanced analysis could use task-specific dimensions.
4A/B comparison. A/B comparison uses the same sensor and environmental signal assumptions for both targets. The geometric range changes with each target's critical dimension; differences in target contrast or thermal signature are not modelled separately.
5Environmental / signal probability assumption. Scales the signal thresholds for a more or less demanding screening assumption — it does not change the geometric pixel requirement. These thresholds, and this scaling, are illustrative and not calibrated. System-level prediction needs MRTD/MRT or validated sensor/atmospheric modelling.

Field of view

Field of view calculator for EO/IR cameras

How field of view is calculated

Worked example

Frequently asked questions

Need the real system, not just the numbers?