Public surveillance has long been a key area of interest for police authorities, who monitor public roads and infrastructure to ensure safe, crime free community living. Traditionally, this has been a manually intensive task performed by human workers. However, in recent years CCTV systems have been increasingly deployed in urban areas worldwide to support these workers. Many studies have shown that the introduction of CCTV cameras has reduced a range of crimes and violations in monitored areas. In the US and the UK, for example, CCTV installations have resulted in reductions in several crimes, especially theft and robberies in high risk zones.
However, this is only a part of the story. These cameras were and often still are monitored by human operators, leading to false alarms due to inaccuracies in detecting abnormal or suspicious activities. With the advent of AI enabled CCTV, autonomous monitoring is now possible. These systems can support a wide range of public surveillance use cases, such as perimeter monitoring, dangerous object detection, vandalism detection, sentiment analysis, and even suicide prevention. AI-enabled CCTV also breaks the link between the number of CCTV cameras installed and the human resource needed to monitor the resulting CCTV feeds.
Transforma Insights recently published a report CCTV: 829 million devices are expected to be used for surveillance by 2035 discussing expected market developments and market forecasts for the period 2025-2035. This blog focusses on AI-enabled CCTV and how advanced CCTV is being used by police authorities to curb crimes.
With the availability of AI-enabled CCTV, real-time monitoring and public surveillance is becoming reality, not only detecting suspicious activity but also alerting the relevant authorities to take necessary action. One of the interesting developments in CCTV is the use of Automatic Number Plate Recognition (ANPR) or Automatic License Plate Recognition (ALPR) for crime detection. Historically, such use-cases have been limited to enforcement of traffic rules, but recently use-cases have been extended to also incorporate vehicle registration details and check with databases if vehicles have been stolen or are associated with past criminal activity. As a result of the matching of vehicle registration details with relevant databases, law enforcement authorities can receive instant alerts.
The design of these systems is often undertaken with the aim of minimising power consumption so that new installations can be deployed with minimal time and effort. The efficacy of such solutions is, however, up for debate as the makers of these systems often claim that they help to solve, for example, 10% of reported crime in the US, while detractors question these claims and often cite privacy violations as a major drawback of deploying smart cameras. Some city authorities in the US are even prohibited from sharing data collected from ALPRs with federal agencies. For instance, in California police agencies are prohibited from sharing data collected from license plate readers with out-of-state or federal agencies. In 2025, state police came under scrutiny for allegedly sharing such data with federal agencies in connection with an Immigration and Customs Enforcement (ICE) investigation.
In the near future, providers of public surveillance camera networks are likely to consider partnerships with smart outdoor camera vendors to enable the integration of video evidence captured across both ecosystems. Such collaborations could help deliver more holistic coverage in urban environments, potentially allowing authorities to monitor larger areas more effectively with a relatively smaller number of publicly deployed surveillance cameras. This approach may help police and federal agencies to strengthen investigative processes by improving access to relevant video evidence.
However, the viability of such initiatives will depend significantly on addressing related privacy, data governance and other regulatory concerns and the willingness of householders to share their video footage as required. If these challenges can be effectively addressed, collaborative models may reduce the need for extensive public CCTV deployments in neighbourhoods that already have a high penetration of connected doorbell and outdoor cameras. But operating such solutions is not simple and, for instance, a partnership between Ring and Flock Safety was established in October 2025 and ended within four months of announcement due to operational challenges associated with managing the integrated solution.
Local authorities can capitalise on their infrastructure for smart cameras by monetising the data captured from their CCTV networks. For example, they can sell anonymised data to businesses to support dynamic real-time advertising campaigns. With this kind of arrangement, a retailer can display targeted advertisements based on the demographics of passers-by. Revenues generated through such commercial arrangements can, in turn, support local authorities to expand and upgrade CCTV deployments.
Currently, short range network technologies, particularly ethernet and Wi-Fi, and also cellular connectivity are primarily being used to connect public surveillance cameras. 5G cellular technologies are expected to play an increasing role in the coming years to support real-time data transfers without the need to set up and manage a Wi-Fi or ethernet local area network. As a result, connected CCTV cameras are expected to become one of the most significant application areas for 5G in the coming years. Since public video surveillance systems span across many large cities around the world and most of them are placed in an outdoor environment, they often benefit from cellular connectivity and 5G technologies can help to ensure the availability of high speed data links.
Public surveillance has always been critical for local government agencies and police authorities to reduce criminal incidents, and increasingly AI-enabled and 5G cellular connected cameras will be deployed to support in such efforts. Solutions such as smart cameras will increasingly play an important role in round-the-clock monitoring, particularly in urban areas, and the notification of relevant authorities on detecting suspicious activity. As AI advances, smart camera solutions will evolve and become more sophisticated to further reduce inaccuracies in the solutions and to better integrate with law enforcement agencies and city management stakeholders.
