Can AI help map out a different future for video surveillance?

Surveillance cameras have come a long way since the days of businesses simply hooking several CCTV units to a monitor in the backroom.

The global video surveillance market is expected to grow at a CAGR of 8.5% between 2024-2030, reaching over $88 billion. In light of this, surveillance cameras and their manufacturers need to carry a high degree of innovation. After all, today’s cameras are business enablers, leveraging new technologies to create added value while reinforcing security and safety.

That degree of innovation is what makes 2025 an exciting year for the surveillance camera industry. Propelled by the growing integration of hardware and software and the growth of AI-enabled solutions, surveillance cameras will help to transform entire business functions and reinvent their relationship with network security.

With manufacturers working to improve their product offerings and integrate new technologies, one might expect those offerings to move out of reach for many businesses. That is not necessarily the case, nor does it have to be.

If nothing else, the surveillance camera industry in 2025 will see more organisations reap the benefits of more remote video monitoring thanks to increased practicality and affordability, as well as being able to adopt AI technologies in a cost-effective way. Businesses can also rely on fewer cameras, as a single unit can cover a large area with high enough image quality. And, in the event of an incident, human operators can be immediately notified and directed to the video images they need to see.

Demand for surveillance cameras is driven by the applications of video data, meaning there is a growing dependence on the analytics that use said data to provide actionable information. Because of this, video surveillance technology is judged according to specific parameters, including resolution, frame rate, and weather hardening.

Image quality is a particular trend in the security sector going into 2025, particularly how images from visual sensors can be viewed and analysed by computers rather than humans. Advances in AI and analytics mean that a high-resolution image can lead to larger amounts of metadata and more accurate object recognition and verification.

Thanks to computer vision, video data can be viewed and analysed continuously instead of just when an incident has taken place.

During the last decade, video surveillance has been transformed by the adoption of cloud computing technology. Cloud computing empowers businesses to scale their surveillance systems to meet ever-changing needs, with added benefits such as the ability to automatically roll out software updates across their systems, as well as centralise video management while retaining remote accessibility.

As the cloud has become more commonplace, so too has edge computing. With the ability to perform functions on the devices themselves rather than on separate, distributed servers, businesses can collect, process and store image insights with less latency.

Both cloud and edge computing help to facilitate the innovation the surveillance camera industry will see, leading to a new kind of security ecosystem that both enhances security functions and helps optimise the overall business.