When we hear the word “neural network” we often imagine something complex, similar to the brain. Indeed, artificial neural networks were created as a simplified model of the human brain. In the case of computer vision, they play the role of “both the eyes and the brain”: the camera captures the image, and the algorithm learns to recognize what is happening in the frame.
The idea is based on learning by example. The system is “fed” thousands or millions of images: people, water, movements, various situations. Over time, the network begins to identify patterns: where there is a person in the picture, which movements look natural, and which may be a sign of danger.
The peculiarity is that neural networks are not programmed manually in an “if-then” style. They build internal connections themselves to find patterns. This is why modern computer vision systems have become so flexible: they are capable of not only recognizing static objects, but also analyzing dynamics, behavior, and context.
The peculiarity is that neural networks are not programmed manually in an “if-then” style. They build internal connections themselves to find patterns. This is why modern computer vision systems have become so flexible: they are capable of not only recognizing static objects, but also analyzing dynamics, behavior, and context.
And while facial or vehicle recognition used to seem like the pinnacle of what was possible, today we are increasingly talking about the application of computer vision in new, very important areas — such as water safety.