Yekaterinburg Expands AI Surveillance Across the City
By the end of June 2026, roughly 1,000 new video cameras equipped with neural network modules for facial recognition will be installed on the streets and in public spaces of Yekaterinburg. The devices will analyze video streams in real time, identifying faces of passersby as part of a broader regional surveillance rollout.
Scaling the “Safe City” Network
The Ministry of Digital Development and Communications of Sverdlovsk Region is building a unified regional video surveillance system. The existing “Safe City” network currently integrates 3,607 cameras across Yekaterinburg, a city of more than one million residents. Authorities report that the system has already helped solve 100 crimes. Officials argue, however, that the current number of cameras does not provide sufficient coverage for a metropolitan area of this size. This summer, approximately 1,000 additional “smart” cameras with facial recognition capabilities will be deployed, and existing hardware and software complexes will also receive AI upgrades.
Authorities expect the expanded system to streamline law enforcement operations. The technology is intended to accelerate the search for missing persons, enable faster detention of wanted suspects and improve overall crime detection rates. Officials aim to extend camera coverage across as much of the urban area as possible to increase public safety. The surveillance network is expected to incorporate cameras at railway stations, in newly constructed residential developments and even through building intercom systems.
Learning From Other Russian Cities
Yekaterinburg plans to adopt practices pioneered in St. Petersburg to address disturbances in residential courtyards. In that city, cameras installed in housing complexes can detect groups consuming alcohol. The system issues an automated loudspeaker warning. If the warning is ignored, police are dispatched.
AI-enabled cameras have also demonstrated measurable impact in the Novosibirsk region. In 2025, authorities identified 2,800 offenders using such systems, a 60% increase compared with 2024. A similar computer vision platform operates in Rostov-on-Don, where officials report a 12% decline in crime. As a result, local authorities there plan to expand coverage by adding 500 additional systems to an existing base of more than 2,000 units.
Moscow represents the most extensive example of digital surveillance expansion. The capital operates approximately 270,000 video cameras. Since the introduction of its surveillance network, registered crime has declined by 23.3%, and crimes committed in public spaces have decreased threefold. Meanwhile, the Ministry of Digital Development is working on a national service to process video data collected from regional systems across Russia. Yekaterinburg, following Moscow’s trajectory, is set to become one of the country’s most densely monitored urban environments.
Domestic Technology and Predictive Ambitions
In the coming years, similar surveillance systems are expected to evolve further, with domestic developers supplying the AI modules. Russian technology platforms are positioned to compete with foreign counterparts. For example, the neural network platform FindFace Multi, currently deployed in Rostov-on-Don, was developed by the Russian company NtechLab.
Developers are now focused on predictive analytics. Beyond facial recognition, they aim to train systems to detect behavioral patterns that may precede criminal activity. Such capabilities could allow law enforcement agencies to respond more rapidly to emerging threats, prevent potential violations and increase clearance rates. Supporters argue that faster identification and intervention can enhance public safety, although such expansion also raises broader questions about data governance and oversight in urban ecosystems.
