Video surveillance is taking on a wider role in railway operations, with AI analytics supporting passenger protection, incident response and equipment inspection. For security systems integrators, the shift brings new opportunities alongside more demanding technical requirements.
Railway video surveillance is increasingly being considered for applications beyond passenger security, as advances in AI analytics and network cameras allow operators to monitor operational conditions and identify potential maintenance problems.
The development is widening the scope of railway surveillance projects, which have traditionally focused on recording incidents inside trains and at stations. Newer applications include automated security alerts, passenger movement analysis and visual inspections of railway equipment.
In an
October 14, 2026 blog post, Jeremy Deage, Global Product Manager at Axis Communications, outlined how surveillance technology could support these functions across railway networks.
The development also presents opportunities for security systems integrators, although deploying network video on moving trains presents challenges that are different from those encountered in conventional building security projects.
Reliable recording, equipment durability, remote connectivity and integration with existing railway systems are among the considerations that can determine whether a deployment meets operational requirements.
Why onboard surveillance requires a different approach
A camera installed inside a train must cope with conditions that would rarely affect a conventional indoor security camera. Constant vibration, electrical interference and temperature variations can influence performance. Outdoor installations face additional difficulties, including exposure to moisture, dirt and changing illumination as trains travel through different environments.
These conditions influence hardware selection and installation practices. Deage noted that camera placement should reflect the intended application. Wide-angle devices, for example, can provide extensive coverage of passenger compartments, while externally mounted cameras may be required to observe train doors and the surrounding environment.
Low-light imaging and wide dynamic range capabilities become important when lighting conditions change rapidly, particularly around stations and tunnels.
Railway-specific standards must also be considered. EN 50155 addresses electronic equipment used on rolling stock, while EN 45545 covers fire protection requirements for railway vehicles.
Axis identifies these standards as relevant to its railway camera portfolio. For integrators, specifying suitable equipment is only part of the task. Installations must also account for cabling, electrical compatibility, mounting arrangements and maintenance accessibility.
Another important requirement is maintaining recording capability when a train cannot communicate reliably with a central monitoring facility.
Deage described an architecture in which local recording equipment retains footage onboard, with network-attached or cloud storage providing additional capacity for
central access and retention.
This arrangement allows video capture to continue independently of a continuous external connection, although actual resilience will depend on system configuration and available storage.
Integrators therefore need to determine how much footage must be retained locally, how recordings will be retrieved and how the system will recover from communication interruptions.
Analytics adds new possibilities for passenger protection
One of the more immediate applications of AI in railway surveillance is identifying events that warrant operator attention. Rather than requiring security personnel to examine numerous camera feeds simultaneously, analytics can flag activity matching predefined conditions.
According to Deage, potential applications include identifying people entering restricted areas, detecting unattended items and recognizing unusual activity around train entrances.
These functions could be valuable in situations where monitoring personnel are responsible for multiple vehicles or facilities. The technology can also support response procedures. When a suspicious event is detected, an alert can direct personnel to the corresponding footage for assessment.
However, automated detection does not eliminate the need for human judgment.
An unattended bag, for example, does not necessarily represent a security threat. Similarly, passenger congestion may reflect normal boarding conditions rather than an emerging safety problem.
For integrators, analytics configuration and testing are therefore important parts of deployment. Detection accuracy must be assessed against the conditions in which the system will operate, while alert procedures should make clear when intervention is necessary.
Video analytics may also offer information that is useful outside the security department. Analysis of passenger movements and station dwell times could help operators identify recurring congestion and examine whether boarding patterns contribute to service delays.
These uses illustrate how a surveillance network might generate operational information in addition to security footage. However, they should not be confused with evidence that such functions have been widely deployed or have delivered measurable improvements across the industry.
A deployment in Croatia demonstrates the value of network video. An earlier railway installation documented by Axis illustrates how IP cameras can serve several purposes within the same train.
Croatian railway vehicle manufacturer KONČAR worked with local Axis partner Elektrokem to install network cameras on trains operating in Croatia and Bosnia.
According to an
Axis case study published in 2020, the project involved nearly 400 cameras across KONČAR trains at the time.
Cameras installed inside passenger carriages supported security monitoring, while external devices provided forward- and rear-facing views for train drivers.
The exterior cameras effectively supplemented drivers' visibility of areas around the train. Video could be viewed in the driver's cabin and recorded for subsequent examination.
The installation also had to address railway-specific engineering requirements, including electromagnetic compatibility and connections between carriages.
The project demonstrates the practical value of combining passenger surveillance with other visual monitoring functions.
However, the published account primarily concerns network video deployment and should not be presented as evidence of an operational AI analytics implementation.
That distinction is important as the industry evaluates increasingly sophisticated surveillance capabilities.
Equipment inspection offers another potential application
Beyond monitoring passengers and security incidents, camera-based systems are being developed to examine the condition of railway assets. One example involves pantographs, the roof-mounted assemblies that collect electrical power from overhead lines.
Because these components operate in contact with railway electrical infrastructure, deterioration or mechanical damage can create problems extending beyond the affected train.
Deage highlighted the use of high-speed imaging and AI analysis to inspect pantographs for wear, missing components and other defects. According to Axis, specialized camera technologies can capture images of equipment on trains travelling at speeds of up to 300 kilometers per hour.
The resulting images can be examined for damage that maintenance personnel may need to investigate. The broader objective is to support maintenance decisions using information about the actual condition of equipment, potentially allowing problems to be addressed before they lead to failures.
Evidence of work in this area also comes from outside the conventional security surveillance industry. In August 2026,
Europe's Rail Joint Undertaking published details of a wayside pantograph monitoring project combining high-resolution imaging, AI-based analysis and three-dimensional reconstruction.
Unlike cameras carried aboard a train, the system examines pantographs as trains pass fixed monitoring locations. Europe's Rail reported testing at facilities in Spain and operational locations in the Netherlands.
The technology was assessed at Technology Readiness Level 5–6, indicating that it remained in an advanced development and testing phase rather than being a fully mature commercial deployment. The project nevertheless illustrates the potential for imaging technologies associated with surveillance to support railway maintenance applications.
Such deployments also demand different competencies from conventional security projects. Camera selection must account for object speed, illumination, image capture requirements and the reliability of defect detection.
Linking video with railway operations
As surveillance applications become more varied, integration with other systems becomes increasingly important. Deage identified video management software as a key element in bringing together footage from different cameras and allowing operators to interact with the information.
For example, integrating video with an emergency communication system could allow a control room to retrieve footage from the location of an activated alarm. Similarly, linking surveillance records with train identification and location information could make it easier to determine where an incident occurred and retrieve the appropriate recording.
These capabilities depend on the underlying systems being able to exchange information accurately. Integrators must consider how events are associated with individual vehicles, how recordings are indexed and whether personnel can access relevant footage without navigating multiple disconnected applications.
The practical challenges become more significant when an operator manages a large fleet with cameras, onboard recorders and software operating across different generations of equipment.
Cybersecurity is another consideration.
Connecting surveillance devices to wider railway networks introduces requirements around access management, device authentication, software maintenance and protection of recorded information.
In March 2026, the UK's Department for Transport issued
cybersecurity guidance for passenger rolling stock procurement.
Although the guidance addresses train systems more broadly rather than prescribing a particular surveillance architecture, it reinforces the need to consider cybersecurity throughout equipment procurement, operation and maintenance.
Video surveillance deployments must also account for applicable privacy and data protection requirements, particularly where cameras record passengers or employees.
What integrators should consider
The growing range of railway video applications suggests that integrators may increasingly be asked to deliver systems supporting several departments rather than security teams alone.
A project might need to accommodate passenger surveillance, driver visibility, event detection and asset monitoring, each with different technical and operational requirements.
That makes early coordination with railway operators important. Security personnel may prioritize incident retrieval and live monitoring. Maintenance teams may require high-resolution images of specific components, while operations managers may be interested in passenger movement data or vehicle-related events.
Understanding these requirements can help integrators determine whether the functions should share infrastructure or operate through separate systems.
It is equally important to establish which proposed capabilities are commercially proven and which require further testing.
AI analytics can extend the usefulness of video, but its effectiveness depends on the accuracy of detection, the quality of the underlying images and the procedures used to act on the information.
The opportunity for integrators is therefore not limited to installing more cameras. It increasingly involves designing dependable systems that deliver appropriate video and analytical information to the people responsible for railway safety, security and operations.
As these applications develop, successful projects will depend on integrating surveillance into existing workflows while meeting the reliability requirements of the railway environment.