The fast progress of artificial intelligence (AI) and video analytics is redefining the rail surveillance landscape. Advancements have bolstered proactive event detection, predictive maintenance, and enhanced situational awareness, according to the International Union of Railways (UIC).[1] Particularly, AI-driven innovations are transforming onboard rail CCTV systems from basic surveillance tools into intelligent solutions that enhance safety, operational efficiency, and connectivity. Leveraging edge computing, 5G, and AI algorithms, these systems enable real-time video analysis, predictive maintenance, and the seamless integration of converged networks.
Moreover, the global drive towards self-driving trains heightens the importance of onboard CCTV systems, which now need to handle high-resolution data, cybersecurity threats, and complex data analysis. Here, tailored solutions, encompassing scalable infrastructure, strategic camera deployment, and advanced cybersecurity, are paving the way for smarter, more secure, and connected rail ecosystems.
How Is Onboard CCTV Transforming Rail Operations?
Autonomous trains, capable of “seeing,” “thinking,” and “acting” on their own, are driving the demand for intelligent CCTV systems. Modern onboard CCTV systems go beyond simple surveillance; these systems integrate advanced computing capabilities into the train’s operations. Many applications often use IP cameras with edge computing and central platforms to distribute core functions, enabling efficient data processing near the source. These systems use local, real-time video stream analysis and distributed processing for uninterrupted performance, even in remote or high-speed scenarios. Meanwhile, seamless train-to-ground communication, enabled by these intelligent platforms, is raising the bar for safety, operational efficiency, and system integration within connected rail ecosystems. This transformation is reshaping the role of onboard CCTV systems in several key aspects:
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Real-time video and immediate response: Real-time video streams enable critical features like train-to-ground transmission, video on demand, alarm notifications, and pre-recording, facilitating timely decision-making during operations.
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Enhancing surveillance with intelligent video analytics (IVA) and AI: AI-powered video analytics in onboard CCTV systems allow for image analysis, data mining, and predictive maintenance, thus boosting operational reliability and efficiency. For example, high-resolution cameras with edge AI analytics can independently detect abnormal passenger behavior or track obstructions, minimizing reliance on centralized processing.
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Post-incident investigations: Onboard CCTV systems facilitate video storage backup and off-loading, enabling operators to review recorded footage during post-incident investigations.
Pilot programs for AI-driven intelligent surveillance systems are underway in multiple countries, for example, Australia [2] and the UK [3]. This means the rail industry is undergoing a substantial transition to smarter, interconnected surveillance technologies. Even so, we still face challenges that must be tackled before full implementation.
1. Complexities of Video Analytics
AI-driven video analytics in rail applications present significant challenges because of the dynamic and varied nature of video content:
- Interior cameras: The difficulty in identifying significant events from irrelevant ones within crowded passenger spaces.
- Exterior cameras: Algorithms processing rapid changes and minimizing motion blur are needed to capture fast-moving scenery above 150 km/h.
Compared to general CCTV, the complex video scenarios in rail onboard CCTV systems make implementing AI video analytics more difficult.
2. High Bandwidth and Storage Demands
Increased rail ridership and safety concerns have led to a substantially higher demand for better images and more comprehensive camera coverage. Advancements in video resolution technology have revolutionized surveillance, progressing from 720p to 1080p, and now reaching the remarkable clarity of 4K, enabling high-definition monitoring with fewer blind spots and greater detail.
However, this leap in visual clarity comes with trade-offs. The sheer scale of data from high-resolution video presents considerable operational challenges. For example, 4K resolution generates roughly four times the data of 1080p, resulting in significantly larger video streams that strain bandwidth and storage resources considerably. Existing network and storage solutions face mounting challenges in managing these demands effectively.
3. Network Convergence and Security
Following the adoption of IEC 61375, rail systems now commonly use a converged network for train control and management system (TCSM) and onboard multimedia and telematic subsystem (OMTS). This integration means CCTV systems share Ethernet backbones with other onboard systems, raising the stakes for cybersecurity and bandwidth management.
1. Strategic Camera Deployment
Expanding AIoT applications and diverse surveillance needs are driving the demand for increased camera deployment on trains. Furthermore, a variety of cameras are required for different installation settings:
- Day/night cameras with built-in IR illuminators for driver cabs.
- High-speed cameras for clear imagery in fast-moving conditions.
- Rugged outdoor cameras that can withstand extreme environments.
2. The Adoption of High-resolution Videos
Video surveillance aims to capture sharp images across wide ranges and long distances. Thanks to fast-growing lens and image sensor technologies, increasingly higher resolution videos are available, regardless of hardware or software capabilities. Onboard CCTV systems are expected to standardize higher resolution for its enhanced detail. For example, 4K (3840 x 2160) resolution has become mainstream in monitors and video displays.
3. Scalable Bandwidth and Storage Solutions
The trend toward higher image resolution in onboard CCTV cameras is supported by the increasing availability of Gigabit network systems, switches with advanced bandwidth management capabilities, and large storage capacities (e.g., 10TB HDD or cloud storage). High-resolution video consumes significantly more bandwidth and storage than lower-resolution formats. Affordable Gigabit networks, combined with switches that provide robust traffic prioritization to ensure smooth video data transmission, make high-resolution video surveillance systems possible. High-definition CCTV cameras’ bandwidth demands are now manageable thanks to these advancements, ensuring reliable network performance.
4. Comprehensive Cybersecurity Solutions
By integrating CCTV systems into converged networks, rail operators are prioritizing cybersecurity measures that align with IEC 61375 standards. The adoption of IEC 62443, with its thorough guidelines for securing industrial automation and control systems, is increasingly complementing these measures. Switches and routers are essential to this architecture, forming the backbone for handling high bandwidth needs and enabling smooth communication between CCTV cameras and other key onboard systems.
Integrating advanced cybersecurity features—device authentication, access control, and real-time traffic monitoring—directly into switches allows operators to create more resilient network infrastructures. Adopting strategies like zero-trust architecture and AI-based intrusion detection further protect the entire system against evolving cyberthreats. This holistic approach reinforces the integrity and reliability of onboard systems, safeguarding both the physical and digital assets of rail networks.
Conclusion
The target for most vehicle suppliers in today’s automotive technology is the development of fully self-driving, Level 5 vehicles. Real-time images from vehicle cameras are crucial inputs to AI video analytics systems, powering immediate driving instructions and decisions. We can expect a rise in demand for more cameras, high-resolution videos, and more robust network solutions as this technology develops.
Integrating advanced CCTV systems is more than a technological upgrade—it is a foundational shift toward a connected, intelligent rail ecosystem. Collaboration among rail equipment suppliers, system integrators, operators, and regulators will be crucial to realize the vision of an intelligent rail ecosystem. With over 15 years of experience in the rail digital market, Moxa is developing 4K onboard CCTV cameras and AI-powered video analytics solutions to improve rail surveillance.
For more information, visit Moxa’s Onboard CCTV microsite.
[1] The journey toward AI-enabled railway companies, UIC, 2024
[2] Ipsotek wins station security contract in Sydney, Railway PRO, 2021
[3] UK’s Network Rail extends AI trial, Railway Technology, 2024