Falcon Tech explains how its city machine vision grew into a network of 4,000 systems

Falcon Tech has described how its machine vision system for the city went from parking enforcement to a large video analytics platform that is already in use in Moscow. According to the company, a network of more than 4,000 hardware-software systems helps record violations, assess infrastructure load and pass data to city services, and a team of more than 140 specialists supports the system.

From parking to a platform

The project started in 2017 with a fairly practical task: the city did not need yet another stream of camera video, but a working tool for managing parking space. It had to be possible to tell whether a spot was occupied, who had parked on a lawn or in a disabled spot, and how to collect evidence without paperwork routine. In 2017–2019 the company built the first 100 systems, which did not simply film the street but automatically generated events for further checks and response.

The main difficulty turned out to lie not in recognition itself but in real-city conditions. Cameras hang at different angles, headlights wash out the frame at night, objects are partly hidden by snow in winter, and rain and fog sharply degrade the image. A separate block of work went into license plate recognition and reducing false positives. In the article the company writes that in 2024 it began working on government contracts directly with the state agency GKU "AMPP", and that the overall scale of the system exceeded 4,000 systems with recognition accuracy above 98%.

"Most violations happen not because someone decided to break the rules

on purpose, but because everyone is used to nobody checking here."

Where machine vision helps

As the project grew, it became clear that the basic architecture suits more than parking. Falcon Tech has effectively turned a point solution into a universal city video analytics platform that can be adapted to transport infrastructure, industrial sites and other scenarios where streaming data and a fast signal about a problem matter.

The company is now betting not on one niche but on a product line, so as not to depend on a single segment and to reuse its existing stack in adjacent tasks. This approach matters for deployment too: it is easier for a customer to extend an installed system than to build a new one from scratch for each case.

The platform already covers, and is preparing to cover, several types of tasks:

• monitoring parking space occupancy and parking rule violations;
• detecting fire, smoke, crowds and other incidents;
• analyzing the load on transport facilities and city infrastructure;
• industrial and site monitoring built on the same architecture;
• future modules for analyzing pedestrian flows, ecology and event forecasting.

The company stresses that this is not only about surveillance and fines. The point of such a system is to reduce manual control, give the city a fuller picture of what is happening and turn video into management data. As an example it cites effects for urban improvement: in 2023, according to the material, 1,100 courtyards were renovated with fine revenue, and in 2025 more than 10 billion rubles earned from paid parking were directed to city works.

The next step is to add more modules related to security, transport and environmental monitoring.

What this means

The Falcon Tech story shows how AI in the urban environment is moving from demos to infrastructure: value comes not from the camera or the neural network alone, but from the combination of models, hardware, integrations and a clear response scenario. It is also a way to build new services on top of already working city infrastructure.

For the market this is an important signal: machine vision in the city is becoming not an experiment but a basic digital layer that can be scaled to transport, construction, security and urban planning.