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.

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.

At first glance, a pool is a controlled, safe environment: there are lifeguards, the depth is known, and people are supervised. But statistics tell a different story: accidents happen even where everything seems to be under control. The reasons are simple: a lifeguard cannot watch dozens of people at the same time, someone can find themselves in danger in a matter of seconds, and it can be difficult to notice this in time.

A drowning person does not always wave their arms and scream, as in the movies. More often than not, the opposite is true: movements become sharp and chaotic, the person loses strength, and the alarm signal may go unnoticed.

This is where a computer vision system becomes an indispensable assistant. It can continuously monitor dozens or hundreds of areas of the pool, detect abnormal behavior, and instantly alert the lifeguard. It is not a replacement for humans, but a tool that reduces workload and response time. In a critical situation, every second counts.

Thus, technology not only helps automate monitoring, but also literally saves lives.

If a swimming pool is a predictable and limited environment, then open water is much more complicated. Lakes, rivers, and the sea have variable conditions: wind, waves, sun glare, and changing transparency. The camera and algorithm have to deal not only with the image of a person against the background of water, but also with a multitude of dynamic factors that can mask or distort the signal.

In addition, in open water, people can be very far from the camera, partially hidden by waves, changing their poses and behavior. What is easily captured from above in a pool becomes a highly complex task in natural conditions.

Nevertheless, technology is advancing. Systems are being developed that take wave dynamics into account, analyze movement in different frequency ranges, filter out glare, and use multiple sensors at once, such as thermal imagers. This makes it possible not only to “see,” but also to truly recognize risk, even in complex and changing environments.

It might seem like a camera + neural network = a ready-made solution. But in practice, it's not that simple. Water creates a lot of visual “noise”: reflections, light play, transparency, bubbles, random objects. All of this can confuse the system.

Another challenge is the diversity of people and situations. One visitor moves abruptly but is just playing; another freezes on the surface and rests. Where is the line between normal and dangerous? The neural network must be able to distinguish such nuances.

"In addition, the algorithm cannot be overloaded with false positives. If the system constantly alerts for nothing, rescuers will stop responding. Therefore, the balance between sensitivity and accuracy is a key task.

Finally, there is the issue of ethics and privacy: cameras record people in swimsuits, and developers are required to comply with data protection rules, storage, and use of video. This imposes additional requirements on the architecture of solutions.

All these complexities do not negate the value of the technology. On the contrary, it is precisely the overcoming of such barriers that drives the industry forward and makes systems increasingly reliable.

When it comes to computer vision in a pool or open water, the first thing to think about is not a neural network, but optics. The camera is the eyes of the system, and whether the algorithm can recognize anything useful at all depends on the quality of the “input image.”

Let's start with cameras. In most cases, IP cameras are used for such tasks, which connect to the network and transmit a video stream in real time. Here, a wired connection has a clear advantage: signal stability. Wi-Fi is convenient, but it is prone to delays, packet loss, and congestion. When every second can cost a life, connection reliability is critical.

But it's not just data transmission that matters. Water creates a complex picture: sun glare, reflections, flickering shadows. To combat this, polarizing filters are used to reduce reflections and make the surface more “transparent.” This is a simple but very effective technology familiar to photographers and cameramen.

In addition, cameras are now used that can shoot not only in the usual visible range, but also in infrared or even thermal. The infrared range helps to “see” a person when the lighting is poor or the water surface causes too much glare. Thermal imagers can detect the temperature contrast between the body and the water.

Thus, choosing the right camera and optics is the first step toward making a machine vision system work in practice, not just in theory.

Today, cameras with built-in neural network modules are becoming increasingly common. They can process images directly inside the device without transmitting the stream to a server. This seems very convenient: minimal delays, less load on the network, compactness.

But this approach also has obvious drawbacks. The hardware capabilities of built-in processors are limited, and they are usually designed for narrow tasks: face recognition, license plate recognition, simple motion detection. It is practically impossible to configure them for something non-standard—for example, complex analysis of video with water, waves, and a lot of visual noise. The cameras are programmed for a specific set of functions and offer little flexibility.

Moreover, if complex neural networks are required—for example, architectures that analyze the dynamics of human movement underwater—the power of the built-in chips is simply not enough.

Today, the real-world solution most often looks like this: cameras capture the image, and all processing is done on an external server with a GPU. Such a server is capable of running large models that analyze not only static frames, but also temporal sequences, movement dynamics, and human interaction with water.

Here we encounter another peculiarity: “water” tasks require much more complex neural networks than, say, recognizing license plates or identifying faces. A car always looks roughly the same, and a face is a static object. In water, everything is dynamic and unstable, so the network must be “heavier” and more powerful.

Of course, there are intermediate options for compact solutions. For example, the NVIDIA Jetson family. These small modules allow you to run neural networks directly on the device, and they are much more flexible than “firmware-based” smart cameras. But there is a compromise here too: power is limited, which means you have to use more compact models.

As a result, developers balance the convenience of embedded solutions, the performance of external servers, and the flexibility of intermediate platforms. And this balance directly affects how reliable and applicable the system will be in real-world conditions.

When the system detects a potentially dangerous situation, the main question is how to alert the rescue worker. The speed of response depends directly on this.

The traditional method is to use screens in a monitoring center, where an operator can see images from cameras and system signals. This approach is convenient for large complexes, but it has a drawback: the rescuer at the scene is not always sitting in front of a monitor. At a swimming pool or beach, a screen is no substitute for live observation.

Therefore, wearable devices are increasingly being used in practice. Smart watches, compact Android devices, special bracelets — all of them are capable of receiving alarm signals in real time. They have several important advantages:

• Versatility — the device can be worn on the wrist or belt, so it is always close at hand;
• Long battery life — modern gadgets hold a charge for a day or more, which is enough for a shift;
• Volume and vibration — it is impossible to miss an alert, even if there is noise around or the rescuer is busy moving.

It is important to emphasize here that the system should not overload the rescuer with a constant visual interface. You cannot put a screen in front of their eyes and require them to watch both the water and the display at the same time.

Not all water safety solutions are based on computer vision. There are a number of alternative approaches.
The first class of such systems are wearable devices for visitors. They can be in the form of a bracelet or a special sensor that reacts to prolonged immersion. If a person stays underwater longer than the permissible time, the device sends a signal to the central control panel.

The advantage here is obvious: there is no need to analyze the image, the system works directly with the fact of being underwater. But there are also disadvantages to this approach: visitors have to wear an additional device, which is not always convenient or pleasant. In addition, such systems are more suitable for swimming pools, where it is easier to ensure that everyone is equipped with sensors.

The second class of solutions is underwater cameras. They do not use neural networks, but simply record the appearance of an object at the bottom of the pool. As soon as the camera detects a silhouette, the system sounds an alarm. Such solutions are known for their high accuracy: there are practically no false alarms, because the immersion of a body to the bottom is an unambiguous event. But there are also drawbacks: installation is expensive, a lot of equipment is required, and underwater maintenance is complicated.

Thus, each technology has its pros and cons. Wearable devices provide direct control, underwater cameras ensure accuracy, and computer vision with neural networks allows for large-scale and flexible operation. In practice, different approaches are increasingly being combined to maximize reliability.

Looking ahead, it becomes clear that safety systems will not be limited to cameras or sensors.
One promising idea is the use of surveillance drones. They can automatically patrol the beach or pool area, change their viewing angle, and, in case of an alarm, head to the scene of the incident. Combined with AI, this allows for a flexible response to the situation.

Another trend is distributed sensor networks. Imagine a complex where cameras, sensors in the water, wearable devices, and even smart bracelets are synchronized into a single system. Artificial intelligence analyzes all the data simultaneously and builds a complete picture of what is happening.

Finally, there is increasing talk that AI will be integrated directly into emergency response systems. That is, not just to record the danger, but to immediately trigger a chain of actions: notify a rescuer, turn on light or sound alarms, and possibly even provide the victim with a rescue device.

As a result, technology is gradually ceasing to be just a “hint” for rescuers and is beginning to turn into a full-fledged assistant. And although there are still many challenges — from cost to privacy issues — the direction of development is clear: smart systems will become increasingly autonomous and reliable, which means they will save more and more lives.

Today, water safety is based on a traditional system: lifeguards on duty visually monitor the water area, respond to alarms, and provide assistance when necessary. In swimming pools, this usually involves several employees distributed across different areas. On beaches or open water bodies, there are observation towers and patrols.

However, this system has natural limitations. Human attention is not infinite: even the most experienced lifeguard gets tired, can get distracted, or simply fail to notice a situation at the right moment. The average response time depends on many factors, from visitor density to weather conditions. And this time is far from ideal.

This is where there is room for optimization. Computer vision technologies make it possible to eliminate “blind spots” and ensure continuous monitoring of the entire water area. The system detects signs of danger and instantly notifies the lifeguard. This reduces the time from the moment a critical situation arises to the moment a person begins to act.

In addition, the response logic itself can be improved. For example, instead of the lifeguard constantly being torn between observation and action, their attention is focused only on confirmed alarms. This reduces cognitive load and increases efficiency.

In the future, such systems may even distribute tasks within the team: whoever is closest receives the signal, while the others receive a backup notification. In fact, this is a transition from individual observation to teamwork supported by AI.

Thus, technology does not replace rescuers, but rather enhances them, helping them do what they do best—save lives—but faster, more accurately, and with less chance of error.

Accidents on water have been happening since ancient times. But it was with the advent of public swimming pools and water parks that the problem became particularly noticeable. It would seem that a controlled environment should be as safe as possible. However, statistics show the opposite: hundreds of accidents are recorded every year, even in swimming pools with professional lifeguards.

The reason is that the process of drowning rarely looks dramatic. Most often, a person simply stops moving actively, and this is easy to miss. Under high stress, a lifeguard may not notice the critical moment.
History shows that each new safety measure, whether it is increasing the number of lifeguards on duty or improving lighting, only partially solves the problem. It is impossible to completely eliminate the human factor. That is why technologies capable of providing constant monitoring and early detection of risks are becoming such an important addition to traditional methods today.

Every innovation faces the question of trust. Some rescuers see computer vision systems as a reliable assistant, while others fear that technology will distract attention or create a false sense of security.

There is a phenomenon of “excessive trust” when a person relies on the system so much that they stop actively monitoring the situation. This is a dangerous extreme. The other extreme is mistrust when an alarm is ignored because it is considered a mistake.

Therefore, the key factor is the correct integration of technology. Systems should not replace rescuers, but only enhance their capabilities. When rescuers understand that AI is a “second set of eyes” that never tires or gets distracted, trust is formed gradually and organically. As a result, technology becomes a partner rather than a competitor.

The future of security lies not in isolated cameras, but in comprehensive solutions. Imagine a swimming pool where a computer vision system works in conjunction with smart lighting, acoustics, and an alarm system. In the event of an incident, spotlights automatically turn on, an alarm sounds, and everyone's attention is directed to the scene.

On beaches, such systems can be integrated with drones and unmanned boats that deliver lifebuoys before a person has even reached the water. And in water parks, the alarm can be sent directly to staff wearables so that the nearest employee can respond immediately.

In this way, we are gradually moving towards “safety ecosystems” where computer vision is just one part of a large smart infrastructure.

Any innovation faces the question: how much does it cost? Installing a computer vision system in a swimming pool requires not only cameras, but also servers, software, and maintenance. For a water park or municipal facility, this may seem like an expensive project.

But here, another perspective is important: how much is a human life worth, and how much is the facility's reputation worth? An accident has not only moral but also legal and financial consequences. If the system can reduce the number of accidents by even tens of percent, its economic efficiency becomes obvious.

In real life, there are already examples of hotels and sports complexes that, after implementing technology, have seen an increase in visitor confidence and a decrease in insurance costs. Thus, investments in safety pay off not only in social terms, but also in economic terms.

The best way to understand the value of technology is to look at specific examples. In Europe and the US, there are already swimming pools and water parks where computer vision systems have been implemented. In some cases, it was possible to detect a critical situation 10–15 seconds before the lifeguard noticed it. This time proved decisive and prevented a tragedy.
Other projects have tried alternative solutions, such as underwater cameras or wearable sensors. In some cases, they have performed excellently, while in others they have proved too expensive and difficult to operate.
These cases show that there is no universal answer yet. Each solution has to be adapted to a specific facility and its characteristics. But the general direction is clear: more and more pool and beach operators are viewing technology not as a luxury, but as a standard without which safety cannot be guaranteed.

The best way to understand the value of technology is to look at specific examples. In Europe and the US, there are already swimming pools and water parks where computer vision systems have been implemented. In some cases, it was possible to detect a critical situation 10–15 seconds before the lifeguard noticed it. This time proved decisive and prevented a tragedy.

Other projects have tried alternative solutions, such as underwater cameras or wearable sensors. In some cases, they have performed excellently, while in others they have proved too expensive and difficult to operate.

These cases show that there is no universal answer yet. Each solution has to be adapted to a specific facility and its characteristics. But the general direction is clear: more and more pool and beach operators are viewing technology not as a luxury, but as a standard without which safety cannot be guaranteed.