With the development of Artificial Intelligence, more and more smart devices have started to appear, video surveillance has not been left out. In this article, we will look at why security specialists prefer AI-powered cameras over other options that use standard machine vision algorithms.
When designing video surveillance systems, designers often face tasks that require automatic license plate recognition, better known as ALPR, and start wondering: which cameras are better to use in their projects? Tasks such as license plate and face recognition place higher demands on the number of pixels per meter and the angles at which the camera should be positioned. For calculating such tasks, there is specialized software: https://www.jvsg.com/alpr-anpr-system-design/
What is the advantage of AI Cameras over Cameras with specialized algorithms?
AI cameras have a much wider range of functions compared to those that use various algorithms. Such cameras can easily distinguish a cat from a dog, detect faces, license plates, or even recognize a weapon in a criminal’s hands, which allows for saving bandwidth by recording only the moments of incidents. They do not react to minor events like falling leaves or changing shadows caused by sunlight, thereby reducing the cognitive load on staff and allowing them to focus on situations where something is actually happening.These cameras show only those camera views where some event is taking place: a new visitor has entered the store, two people are standing face-to-face, a theft has occurred, or there’s a fire. They are also capable of automatically notifying human personnel in case of an emergency. It makes possible to avoid storing a huge amount of video footage and instead keep only the important moments.
How exactly do they work?
Cameras with simple algorithms have their advantages: they operate significantly faster. Algorithms work more reliably under predefined conditions. Let’s look at an example of how a motion detection algorithm works:
- The camera captures an image
- The camera captures a second image
- The camera compares the two images for changes
- If the change is large enough, it determines there is motion in the frame
From this algorithm, it’s clear that if there is any change in the frame, the camera might trigger falsely — for example, due to moving leaves or changing shadows.
Neural networks, on the other hand, work quite differently and more complexly:
- The camera captures the first frame
- The camera captures the second frame
- Each frame is processed by a neural network. It looks for people, animals, and other objects
- If the neural network detects relevant objects in the first frame, it assigns coordinates to each object
- If there is a change in coordinates in the second frame, it means motion has occurred
The advantage of using neural networks is their accuracy and flexibility. The disadvantage is slower processing speed.
Conclusion
We can conclude that in cases where it is important to accurately determine whether there is movement and what objects are in the field of view, it is better to use AI-powered cameras. However, if the goal is to quickly detect any movement, cameras with specific algorithms are more suitable.
The design of such systems is facilitated by the IP Video System Design Tool, which uses 3D visualization and a 2D layout to design video surveillance systems, allowing for a clear understanding of how well a license plate will be visible — helping to avoid errors related to blurry images.