Thermal cameras, when combined with video analytics, have long been considered the best way to detect people in the outdoors. At one time, they were used primarily only for the most critical sites. But innovations and advancements, along with price reductions, now make them available for more common security applications, like for preventing theft and vandalism. We’ve written this article to help explain how a thermal imaging camera works and why it represents a great choice for outdoor security.
Thermal Cameras “See” Heat
Our eyes work by seeing contrast between objects that are illuminated by either the sun or another form of light. Thermal video cameras work by “seeing” heat energy from objects. All objects – living or not – have heat energy that infrared cameras use to detect an image. This is why a thermal imaging camera can operate at all times, even in complete darkness.
Because they act like a heat vision camera rather than one which uses 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 video 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 for thermal imaging cameras
For these reasons, thermal energy cameras are known 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 presenting the narrow range of temperature differences into a useful image. The result is sometimes 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 can make it difficult for video analytics to detect intruders accurately.
To help illustrate, the capture shown depicts a daylight image from a thermal video camera which cannot effectively compensate for white-out from thermal loading. 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. In this situation, with cameras not designed to deal with thermal loading, the video analytics will have a difficult time detecting intruders with the reliability needed for security.
How Video Processing Makes Thermal Video Cameras Work Better
Thermal imagery is very rich in data, sensing small temperature variations down to 1/20th of a degree. Thermal imaging 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. Such compression is also used by some camera to send video over the network for video content analysis.
The image below shows the 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 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 heat sensor cameras with a high-level of image processing can emphasize small variations between objects and the background to exaggerate the fine details in contrast to other image features, while automatically detecting intruders accurately, every day, every night, under all outdoor conditions.
The image below shows how image processing can overcome outdoor issues and provide a very clear thermal image. The left shows a 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 will better reveal potential intruders.
Using Thermal Imaging Cameras with Video Analytics
Combining thermal imaging cameras with video processing is a great way to improve their ability to work better under even difficult conditions. When paired with video analytics, they can detect intruders over very large outdoor areas, ignoring all the movement – headlights, reflections, small animals, trees and blowing trash – that cause alarms with visible light cameras. This means that you can detect more reliably, over larger distances, even in difficult situations.
This is why SightLogix incorporates a high degree of processing power inside our smart thermal cameras. This processing is used to filter the effects from wind, lighting, precipitation, moving clouds, shadows and vibrations from causing nuisance alerts. The system’s ability to accurately detect intruders with minimal nuisance alarms is the reason many organizations have come to trust SightLogix for their outdoor security applications.
Learn more about SightLogix thermal solutions here.