Long-Range Camera Performance: Understanding Ratings and Specifications (DRI, DORI & PPM)

The difficulties with comparing and rating long range cameras

There are a great many variables that affect the image quality of long-range cameras. Some of these variables are straightforward and measurable, but many are not. Likewise, some of these variables are controllable while others are not. This is why answering the questions like how far my camera can see is very difficult and why there are many different standards and methods used.

First off are the physical components of the camera. Starting with the build of the lens, each lens will vary in how much it magnifies the image (which is always specified), how much light is passed through the lens (which is sometimes specified), and how sharply and accurately the lens focuses that light (which is rarely specified). These specifications not only vary greatly from model to model but even slightly from lens to lens of the same model. Then you have the image sensor that captures the focused light. These vary by resolution (which is always specified), sensitivity (which is specified but not measured equally), sensor size (which is vaguely specified based on approximate 1950s equivalents), image processing (which varies by manufacturer) and color accuracy (which is never specified). So just in the physical components of the camera there are a lot of compounding variables that can result in two cameras with exactly the same specifications having fairly different levels of performance.

Then you have atmosphere and weather. Even in the best conditions, the air you’re looking through is made up of molecules that degrade the image by hindering the transmission of light. Humidity, rain, snow, dust and fog will reduce the image contrast, and atmospheric turbulence from heat variations throughout the scene will cause distortions in the image. The further you’re looking, the more these problems are amplified, and these factors differ not only from location to location, but even minute by minute in the same location.

Example of atmospheric distortion in very long range imagery at 20+km. Note that distortion levels are often much lower in the infrared wavelengths, which is why we recommend multi-sensor systems for most long-range PTZ cameras.

The Thermal (MIWR) camera is less affected by atmosphere than VIS/NIR it is why its important to use different wavelengths.

Note that these atmospheric variables affect all surveillance cameras. All cameras are designed to capture a specific range of wavelengths (such as VIS, NIR, SWIR, MWIR and LWIR). Any two cameras using the same wavelength of light will suffer the same atmospheric limitations. It’s important to keep in mind that atmosphere and weather will affect real world performance more than what is seen in most demo videos, which are often picked from moments of ideal conditions.

To keep things fair and simple, we use a measurement called Pixels Per Meter (PPM) to compare camera performance. It helps to think of PPM kind of like gas mileage. It gives an idea of the performance you can get under optimum controlled conditions, but many factors (driving habits, snowy roads, traffic conditions) might make it unlikely to achieve those results regularly in the real world. That doesn’t prevent it from being a helpful benchmark that we can use to compare competing models in a fair and controlled way.

Pixels Per Meter (PPM)

Pixels Per Meter (PPM) is a measurement that defines the amount of pixels of definition across a 1m width at a specified distance from the camera. It is the simplest way to measure the long-range magnification capabilities of a camera, allowing for easy comparison between different types of cameras. PPM is an objective measurement of detail that takes into account the lens, resolution and sensor size of the camera. It is a fair comparison measurement as it avoids terms that can be misleading to customers such as X-factor or skewed by interpretation like DRI (which is usually calculated with unrealistic assumptions to maximize range in literature and brochures). Note that Pixels Per Feet (PPF) can also be used, replacing 1 meter for 1 foot.

Unfortunately due to the differences in how visible and thermal sensors are measured, PPM calculations are calculated a little differently for the two technologies.

For visible/NIR sensors, Pixels Per Meter is calculated using the Field of View and Sensor Resolution.

Field of View

The field of view (FOV, also called angle of view) is the width of the scene that a camera detects on its sensor. It is determined by the focal length of the lens in relation to the sensor size. Longer lenses or smaller sensors produce narrower fields of view, while shorter lenses or larger sensors produce wider fields of view.

A smaller field of view means that a camera is more “zoomed in” (to use a term that most people are familiar with). For example, a camera with a 90° horizontal field of view (HFOV) will see a 1000m wide section of a wall that is 500m in front of it. If you then adjust that camera’s HFOV to only 1°, it will fill the screen with an 8.7m wide portion of that same wall. This second “zoomed in” field of view is what customers are looking for when they want a camera that can see a long distance. They want a narrow field of view.

Sensor Resolution

The other contributor to long-range camera performance is the sensor resolution. This determines the level of detail within a camera’s field of view. For example, using the 1° HFOV result from the previous paragraph, a newer HD sensor and an old analog CCTV sensor will both produce images that fill the screen with 8.7m of the wall. The analog sensor has a horizontal resolution of 640 pixels, which means it displays 640 segments of detail across that 8.7m scene. This works out to 73.5 pixels per meter (640 ÷ 8.7). The HD sensor on the other hand, with a horizontal resolution of 1920 pixels, is squeezing far more pixels into the same amount of space on the sensor. These smaller pixels provide 3 times the level of detail with a value of 221 pixels per meter.

The images below show the importance of taking resolution into consideration. Using the same hypothetical cameras from the previous example, an American license plate would take up 3.5% of the screen width on both cameras (it would be the same field of view), however the older analog sensor would render that plate with only 242 pixels of detail (22×11), while the HD sensor would render it with over 2,200 pixels (67×33). This distinction is the difference between a blur of pixels and a clearly readable plate.

Pixel Pitch (How Thermal Sensors Differ)

Thermal imagers are measured differently than standard visible cameras, and instead of a dimension for the overall sensor size, they are measured by pixel pitch.

The pixel pitch is the width of each pixel in the sensor, typically designated in microns (μm). This means that instead of different resolutions changing the size of the pixels on the sensor, a different resolution on a thermal sensor will change the camera’s field of view, since it would mean a sensor that has a larger or smaller overall size (see example below).

The good news is that this means only the focal length of the lens and the pixel pitch of the sensor are needed to calculate the PPM. The bad news is that it makes things much more confusing for end users to understand.

DRI & DORI Explained

If you’ve done any research comparing thermal cameras, you’re likely to have come across the term “DRI”, which is often used to compare performance between thermal imaging cameras. DRI stands for Detection, Recognition and Identification, however it’s important to understand how those terms are defined, as they’re likely to mislead many customers with unrealistic expectations. (We explain these definitions in more detail below.)

As for more common color surveillance cameras (visible/NIR), there is another standard that sounds similar to DRI, but is different in its definitions. This standard is called DORI, which stands for Detection, Observation, Recognition and Identification. While most of the terms may be the same word, it’s important to recognize that their specifications are quite different.

Visible/NIR vs Thermal

Visible/NIR refers to standard surveillance cameras that utilize the visible light wavelength and the near infrared wavelength to produce color video images by day and low-light black and white images at night. Thermal refers to cameras that operate in the mid-wave or long-wave infrared wavelengths where radiated heat is transmitted. Thermal cameras detect the “heat signatures” of objects in the scene and produce an image based on their apparent temperature

These two technologies are quite different. They differ not only in the look of the images they produce but even more in how those images can be useful in surveillance. Thermal imaging excels in the detection of threats or targets, but falls short if you’re wanting detailed images of those targets, whereas VIS/NIR imaging can provide excellent detail for identification of long-range targets while being much harder to use for detecting threats in many scenes.

This is why we build many of our camera systems with side-by-side thermal and VIS/NIR imaging. The ability to view both of these imaging feeds simultaneously is a huge benefit for most clients.

The table below shows the difference in common detail requirements for each standard in pixels per meter. Keep reading to see why these differences exist.

What is DRI?

DRI is a universally accepted set of standards that attempts to provide a means of measuring the distance at which a thermal sensor can produce an image of a specific target. These standards were initially developed by the US Army in the 1950s and unfortunately have not been updated much to account for newer technology with larger theoretical ranges that are less likely to be achieved in the real world.

The model is based on a 50% probability of achieving the following objectives, under ideal conditions. Please read each definition as they may not match what you would expect from reading the terms alone.

Detection

Detection refers to the distance at which a target initially appears in the image. This “target” is something out of the ordinary that is warmer or cooler than the ambient environment. Specifically, it will be visible on at least two pixels, so there will not be enough information to confirm what the target is at this distance, just that something is there.

Recognition

Contrary to what might be expected, recognition does not mean that you can recognize an individual. Recognition refers to the distance at which you can determine an object’s class (is it human, animal or vehicle).

Identification

Identification refers to the distance at which you can differentiate between objects within a class. For example, identifying the type of vehicle (truck, SUV, or car) or whether the human is a soldier or civilian.

Note that these distance measurements are based on a 50% probability and do not take any atmospheric conditions into consideration. Weather is almost never ideal so in real use these distances are almost always shorter than specified.

An outdated specification

So the terms detection, recognition, and identification can be misleading, especially to end users who do not have a military or electro-optics background. To make matters worse, the original 1950s specifications were based on old sensors and screen display technologies. The increasing resolution of thermal sensors has shrunk the size of the DRI areas in relation to the overall field of view to tiny specks of white on the screen.

For example, our high-res uncooled thermal sensors have a resolution of 1280×1024, which is over 1.3 million pixels. Human “detection” only requires the target to be visible on 3–4 of those pixels. This is an extraordinarily small portion of the screen (0.0003%) that can easily go unnoticed by the human eye.

Even when magnified, the amount of detail visible at the DRI distances is not as high as one might expect, as seen in the chart below.

Note that while it may seem impossible to tell that a human or vehicle is present in some of these images, it’s often the movement of pixels in an area that makes it more obvious that an object is present. In addition, these standards were developed with trained professionals in mind, who will be able to spot activity much easier than a civilian. The video below gives an example of what human recognition/detection look like in motion (video is taken from a distance of 5.5km, the thermal camera used is rated for 2.6km of human recognition / 7.8km of human detection).

Alternate Methods

The examples above show simulated detail levels at the distances Infiniti uses for DRI, but it’s important to know that different manufacturers may use different methods to determine their DRI numbers.

For example, L3 and FLIR have cameras that are almost identical in specification and should be within 5% of each other, but due to different assumptions on the conditions and atmosphere, one is rated for 21km of detection while the other is rated for 52km. Clearly these ratings are too arbitrary for proper comparison which is why for the sake of transparency and simplicity, Infiniti bases our DRI numbers on pixels on target, however pixels on target is not the only method available.

NVThermIP

The US Army has an updated thermal ranging model called NVThermIP, which is more mathematically sophisticated in order to yield more accurate results. It requires inputting a variety of values that specify the camera lens, detector, framing and sampling electronics, signal processing electronics, viewing display, atmosphere, target and task. The problem is not only are these a lot of inputs, but quite often those values aren’t even known. In addition, there are so many variables and equations at play that it’s often unclear what’s going on.

Pixels on Target (PPM)

Used by many manufacturers as a simpler alternative to NVThermIP, this method is done by calculating the number of pixels needed across the critical dimension of the target and then converting that value to the required pixels per meter. The sensor and lens measurements then allow us to easily calculate the pixels per meter performance of the camera across a full range of distances and determine the distance where each level of detail is achieved.

Of course a drawback to this method is that variables like weather and atmosphere are not included in the calculation, but those values are rarely accurately known and can vary greatly anyways. Additionally, any two cameras using the same wavelengths will suffer very similar degradation and transmission loss through the atmosphere, making it a moot point for camera comparison.

At Infiniti, we calculate the critical dimension by using the square root of the target height multiplied by its width, resulting in a critical dimension measurement for a human at 0.95m and a military vehicle at 2.2m. Using the Johnson Criteria of one line pair for detection, 3 line pairs for recognition and 6 line pairs for identification, and assuming one line pair is equal to two pixels, the required DRI values for PPM are as shown in the chart below.

Visible (Color) & NIR Imaging

The DRI system helps to specify those terms for thermal imaging so that everyone is on the same page, but visible (color) imaging performs very differently. While two pixels on a screen might be enough to count as “detection” on thermal, that level of detail would be nearly impossible to use for detecting a target on a visible imaging system, unless you already knew exactly where to look.

So we need a different set of values for visible color imaging, but as the previous pages show, one of the difficulties that arises when trying to specify detail requirements is that everyone’s idea of adequate detail is different. The problem with using terms like “recognize” or “identify” is that people get an idea in their head of what level of detail that would be and it often differs.

Since perceived detail levels vary greatly, we use the pixels per meter (PPM) measurement to specify the amount of detail needed for each of our clients. We highly recommend specifying your camera performance requirements in PPM, as it makes things clear for everyone involved. Shown below are examples of a few different PPM levels; we’ve also created a document that can be sent out upon request that displays a wider range of detail levels.

An International Standard

If you’re unsure of what level of what PPM levels to specify and are wondering what others use, we refer you to a widely used standard for detail level requirements in visible imaging surveillance systems.

The DORI standard was developed by the International Electrotechnical Committee (IEC), European committee for electrotechnical standardization (CENELEC) and British Standards Institution (BSI). The IEC EN62676-4: 2015 International Standard defines specific levels of detail for Detection, Observation, Recognition and Identification.

Whichever PPM values you decide on, using specified PPM values makes it possible to select specific cameras and calculate their PPM performance to verify that it will provide the performance needed for your application.

Just scratching the surface

We wrote this white paper to explain the rating systems we use while also giving end users an idea of the actual performance they can expect from their thermal and visible/NIR cameras. That said, this information just scratches the surface of the many considerations that go into building long-range surveillance cameras.

In addition to VIS/NIR and thermal, Infiniti also offers active IR illumination, SWIR, and other sensors such as LRF, Radar and more. We do not limit our customers to any one technology; rather we custom build solutions that typically use multiple sensors depending on the project.

There are also many additional aspects to consider when building a camera system, such as cooled vs uncooled thermal, size and weight, stabilization, connection and control, environmental and ingress protection, international trade regulations and more; all of which need to be considered alongside budget and availability.

At Infiniti we excel at designing customized systems to suit your specific needs. We can examine your situational requirements, location conditions and project budget and recommend the best system (or systems) available within that budget. We are also experienced in configuring multi-camera systems complete with multichannel recording, wired or wireless networking, access control, radar integration and much more.

Contact us today for our expert advice on the ideal solution for you.

Emial: [email protected]

www.infinitioptics.com | 1-866-969-6463