IP CCTV: What does pixel density mean exactly?

An IP surveillance system may be used to observe and protect people, objects and people’s activity inside and outside the objects, traffic and vehicles, money handling in banks, or games in casino environment.

All of these objects of interest may have different clarity when displayed on a workstation screen. The image clarity depends primarily on the camera used, the imaging sensor, its lens and the distance from the object.

There is one parameter in IP CCTV that expresses the image clarity in a simple way with just one parameter: pixel density. The pixel density is usually expressed in pixels per metre (Pix/m), at the object plane, although it can be expressed in pixels per foot.

Pixel density in IP CCTV sense should not be confused with the display pixel density quoted by various LCD display manufacturers which defines the screen density, in pixels per inch (PPI).

The advantage of expressing object clarity with its pixel density is that it combines the sensor size, pixel count, focal length and distance to the object in just one parameter.

When using pixel density metrics all variables are included and makes it universally understandable what details you will get on an operator’s workstation screen.

When designing a system, or a tender for a system, one can request pixel density for a particular image quality. So, instead of asking for a 6 mm lens for your camera in a particular location, for example (which means nothing without knowing the camera sensor it is used on), it would be much more useful if a particular pixel density is defined for the view.

This will then be used to calculate the required lens for the particular camera used and the distance from the object. This will guarantee the clarity of the image (assuming the lens is focused optimally and there is sufficient light, of course). Pixel density can be used for any object that IP CCTV user might be interested in: face, licence plate, playing card, money and similar.

Let us now explore how many pixels per metre are attributed to various objects. One of the most commonly referred pixel densities is for Face Identification.

Face Identification in CCTV means sufficient clarity of the image so that one can positively identify who the person on the screen is. According to Australian Standards AS4806.2, for Face Identification in analogue CCTV, we require 100% person’s height to fit on the monitor screen display.

The details of 100% person’s height on a screen have been tested many times and it’s been verified that they are sufficient for such a person to be identified. We know that PAL signal is composed of 576 active TV lines, so, according to AS4806.2, a person’s height would occupy all of the active lines to make it 100%.

Head occupies around 15% of a person’s height, which is equivalent to around 86 lines (576 x 0.15 = 86.4), which is the same when converted to pixels (assuming recording is made full TV frame mode, which is equal to two TV fields).

If we agree that an average person height is 170 cm, the head would occupy around 25 cm of that. The pixel density at the object, which is required to make a positive Face Identification according to AS 4806.2, can be calculated to be 86 pixels at 25 cm of head height. Since there are 4 times 25 cm in 1 m of height, this becomes 4 x 86 = 344 pix/m.

So, one can say that with pixel density of 344 pix/m at the objects plane it should be possible to positively identify a face, according to AS4806.2.

Face Identification as per AS4806.2

Some other standards may require different values, and one such (newer) standard is the IEC 62676-4, which defines 250 pix/m to be sufficient (i.e. suggests that with slightly lesser pixel density than the AS standards one should be able to identify a person).

Clearly, this number is not fixed in concrete, and it will depend on the observing ability of the operator, as well as other parameters (lens quality, illumination, compression artefacts, etc…), but the key is to understand that such a pixel density can be calculated for any type of camera, irrespective if that is SD, HD, 4k or any other format.

The next image quality down, as defined by the standards is for Face Recognition. The details of Face Recognition image should be sufficient to recognise the gender of a person, what he/she is wearing and possibly make an assertion of who that person might be, if picked from a bunch of people that have already been identified somewhere else (e.g. passport or drivers licence photo).

This is basically an image with half the pixel density to the face identification, which according to AS4806.2 should be around 172 pix/m, while IEC62676-4 suggests 125 pix/m.

Similarly, pixel density can be defined for vehicle licence plates visual recognition (not software automatic LPR). In the AS 4806.2, this is defined as 5% characters height on a display screen, which is around 30 TV lines (pixels) (to be very accurate 576 x 0.05 = 28.8).

If we assume that a typical Australian number plate has characters of around 90 mm in height, than this equates to 11 x 30 pixels = 330 pix/m. The number 11 is obtained from dividing 1000 mm (1 m) with 90 mm. One may say that for visual licence plates recognition similar pixel density is required as for face identification.

Licence plate recognition as per AS4806.2

When money and playing cards are observed in banks or casinos, many practical tests have shown that at least 50 pixels are required across the notes or cards longer side in order to positively identify the values. Standard playing cards dimensions are B8 according to ISO216 standard, which is 62 mm x 88 mm.

So, we need the 88 mm card length to be covered with at least 50 pixels for proper identification. This means around 550 pix/m (1000 mm / 88 mm = 11 => 50 pix x 11 = 550 pix/m) should be sufficient for playing cards.

We may require slightly better pixel density for identifying money, since notes size is typically larger than playing cards, so if one takes the Face Inspection pixels density of 1000 pix/m, it should attain pretty good identification, although as it can be seen from the real life example below, even 770 pix/m might be sufficient.

Playing cards and money shown above with 770 pix/m

As it can be concluded from the above examples, the pixel density can be defined for any object and any camera, large or small.

The beauty of the pixel density parameter is, as said at the very beginning, that includes all parameters influencing the clarity of the observed objects.

For this reason, ViDi Labs has developed the ViDiLabs iOS calc (search the iTunes App Store under “ViDiLabs calc”), a unique tool for the surveillance industry, which can also be used in cinematography, photography and any other imaging application dealing with objects details.

So the following table can be used as a rough guide for various pixel densities.

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