Our eyes work by seeing contrast between objects that are illuminated by either the sun or another form of light. How thermal cameras work is by “seeing” heat energy from objects. All objects – living or not – have heat energy that thermal cameras use to detect an image. This is why thermal cameras can operate at all times, even in complete darkness.
Because thermal cameras work by “seeing” heat rather than reflected light, thermal images look very different than what’s seen by a visible camera or the eye. In order to present heat in a format appropriate for human vision, thermal cameras convert the temperature of objects into shades of gray which are darker or lighter than the background. On a cold day a person stands out as lighter because they are hotter than the background. On a hot day a person stands out as darker because they are cooler than the background.
Outdoor challenges can impact how thermal cameras work
For these reasons, thermal cameras have become a good choice as a sensor for “seeing in the dark” because at night background objects tend to be cooler than a person at 98.6 degrees. Under ideal conditions, people are well emphasized at night because they appear brighter than the background and stand out, even in zero light.
However, outdoor security conditions are rarely “ideal”, especially during the day when darker objects absorb the sun’s energy and heat up, an effect known as Thermal Loading. When objects in the scene become uniformly hot in any given area, many cameras have difficulty mapping the narrow range of temperature differences into a useful image. The result is an image with large areas that look “whited out” or “grayed out” and undefined. This makes it difficult to see what is happening in the scene, and it makes it difficult for smart thermal cameras to automatically detect intruders accurately.
The capture at right shows a daylight image from a thermal camera which cannot effectively compensate for white-out. Details such as the power lines, pavement, and other objects have become impossible to discern due to the effect of thermal loading. It’s even difficult to tell that this is a daytime image.
Lack of image clarity can reduce security effectiveness. Security personnel who have to view blurry, undefined video even on a single monitor can become fatigued and confused by images that are not as intuitive as they would be with daylight cameras, while on-board video analytics will have a more difficult time detecting intruders.
Video Processing and Thermal Cameras
Thermal imagery is very rich in data, sensing small temperature variations down to 1/20th of a degree. Thermal cameras must convert these fine temperature variations – representing 16,384 shades of gray – into about 250 gray scales to more closely match the capability of human vision to decipher shades of gray. The image below shows the eye’s difficulty distinguishing between close levels of gray. The top row shows six levels of gray which the eye can see. The bottom row shows sixteen shades of gray – you can see how it is increasingly difficult to distinguish where the shades transition from one block to the next. Consider the fact that a thermal imager has 16,000 shades of gray, over 1000 times more than show in the lower bar graph, and the magnitude of the problem becomes clearer.
In the past, most thermal cameras converted this data in a simplistic way by mapping gross areas together that are close in temperature. This is why thermal images often look blurry, lack detail and conceal intruders, while the analytics would often misdetect intruders entirely.
New cameras with a high-level of image processing can emphasize small variations between objects and the background to exaggerate the fine details and present a clearer image in contrast to other image features, while automatically detecting intruders accurately, every day, every night, under all outdoor conditions.
The image below shows how a thermal camera with image processing can overcome outdoor issues and provide a very clear thermal image. The left shows a thermal camera which lacks the processing to create good contrast and displays objects as “whited out.” On the right, the same image has been intelligently remapped by image processing to emphasize the small temperature differences in the hotter objects, presenting an image that approaches a black and white photo, which is more comfortable to the eye and reveals potential intruders.