In every photograph ever taken, light plays a very important role. When it comes to surveillance systems, IP cameras have a lot of challenges to overcome in the face of changing and complex lighting conditions. Trains, for example, pose many interesting problems for the algorithms operating to capture clear and accurate footage. Today, we’re going to be examining those problems in greater detail. Let’s get started.
What make trains such challenging subjects for IP surveillance?
The light in a tunnel is often limited at best, making it a difficult resource for IP surveillance technology, or more specifically, the Auto Exposure (AE) algorithm. As the trains move, the lighting around them fluctuates quite a bit. As passengers get onto and off of the train, the people wearing light-colored clothing also register to the cameras as a change in light. All of these factors and more create challenges for the Auto Exposure tech.
What does Auto Exposure technology do?
As light comes into the sensors on the camera, the AE technology adjusts the IRIS, shutter, and gain. For reference, the AE algorithm uses an ideal photograph taken in ideal light conditions to compare against. However, this algorithm can be overly sensitive, picking up on lighting changes in a small part of the prospective image and changing the overall exposure. If the AE reacts unnecessarily, it often changes the images it is taking to the point of them being useless.
Camera tampering security can also be tricky.
There are measures in place to prevent people from tampering with the cameras, but many of the same issues remain. When light fluctuates too much, the tampering prevention measures are activated and personnel are alerted, however the technology has a lot of false positives in complex lighting environments like trains. Sometimes, it is very challenging for the camera to know whether or not tampering is in process.
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