“The security industry, with its security cameras, as we know it today, will not exist in 50 years’ time”, said Hiroshi Sekiguchi, VP, Strategic Partnerships and Chief Product Ambassador at i-PRO. That was the thought-provoking statement shared during a forward-looking discussion at i-PRO’s November 2024 press trip in Japan. The panel, featuring Sekiguchi (aka Huey), and Doug Hansel, Technology Evangelist at HighRez, delved into the future of security technology and how advancements in automation, AI, and edge computing could completely transform the sector.
Sekiguchi began by outlining i-PRO’s mission: to contribute to a safer society through advanced image sensing. However, he speculated that in half a century, the security camera concept might be obsolete. While today’s solutions heavily depend on hardware, the rapid evolution of edge computing and AI will push the boundaries of what is possible in automation and surveillance.
Automation and the Shift to the Edge
Automation is reshaping countless industries and security is no exception. Hansel noted that the security industry tends to follow other sectors but has historically been slower to adopt change. Nevertheless, the momentum towards automation is undeniable, with autonomous vehicles serving as a prominent example of what’s possible.
In the context of security, automation is powered mainly by analytics at two levels: on the edge and on-premises. Edge-based analytics are particularly appealing for their ability to process data quickly with minimal latency, enabling real-time responsiveness. Cameras equipped with advanced chips like Ambarella are no longer merely passive observers. Instead, they are evolving into powerful servers capable of running sophisticated AI algorithms directly on the device.
Hansel expanded on this idea, explaining that the growing intelligence of edge devices is revolutionising their role. “With edge computing, cameras become part of a broader, decentralised computing network. They’re not just sensors—they’re active participants in data processing,” he said.
Beyond Surveillance: Cameras as Cluster Computers
One of the most transformative trends discussed was the convergence of security technology and data-driven business models. As edge devices like cameras become increasingly capable, they are starting to resemble cluster computers. Intelligence at the edge means cameras can share computational tasks, creating a distributed processing environment.
A significant step in this direction is i-PRO’s integration of Docker containers into its X Series AI-enabled cameras. Docker containerisation provides a secure and modular environment for running custom applications, offering developers the ability to enhance AI capabilities on the edge. By isolating core camera functions from new applications, Docker ensures cybersecurity while also allowing seamless integration with cloud services. Docker also has the potential to cluster the computational power of a group of edge devices together into a ‘swarm’ that can work on a shared task beyond what an individual device might be capable of.
This architecture offers several advantages. For developers, it simplifies the creation of tailored analytics applications. Using platforms like Microsoft Azure IoT Edge, custom AI apps can be designed and deployed directly to devices. For integrators and end-users, it transforms cameras into IoT data collection hubs capable of integrating video and AI metadata into larger digital ecosystems.
In a press release, Hideo Noguchi, i-PRO’s Chief Technical Officer, explained: “Docker containers simplify and reduce the cost of developing custom AI apps, making advanced analytics more accessible to everyone.” By enabling this flexibility, i-PRO positions its X Series cameras not only as tools for traditional security applications but also as critical components of broader IoT and digital transformation initiatives.
The Role of AI in Adding Context
Artificial intelligence is playing an increasingly important role in making sense of the massive amounts of data generated by security systems. While current AI applications are primarily task-focused, the future lies in creating AI systems capable of understanding context.
Single-threaded AI and multi-modal AI highlight two distinct paths in artificial intelligence, each suited to specific challenges. Single-threaded AI is like a specialist: highly effective but focused on just one task at a time. Think of a virtual assistant that can set reminders but can’t interpret images or a tool that recognises objects in pictures without understanding the context. It’s a bit like a calculator—brilliant at maths but unable to tackle anything else.
However, multi-modal AI is more like a teacher combining different skills to solve complex problems. It simultaneously processes several types of data—text, images, audio, and more. For example, autonomous vehicles use a mix of camera feeds, radar signals, and GPS data to make driving decisions. As Hansel suggested, future systems could go even further, integrating live weather updates, social media feeds, and breaking news alerts to provide context-rich insights.
While single-threaded AI has its place in focused applications, multi-modal AI represents a shift towards technology that feels more intuitive and adaptable—offering the kind of versatility we expect as automation becomes a more significant part of our lives.
Hansek said, “How we (humans) get the all-important ‘context’ for our own decision-making is by analysing multiple types/sources of information. As AI becomes more able to analyse multiple inputs of data simultaneously, this contextual awareness will enable it to make much better recommendations to operators on the correct course of action for many types of events, perhaps even making some low-level decisions of its own.”
In security, this advancement could lead to real-time packet analysis and intrusion detection, reducing the latency issues that often hinder network-based solutions. The ability to process multiple streams of data concurrently would improve efficiency and responsiveness, paving the way for smarter, more integrated systems.
A Changing Industry Landscape
While technological advancements offer exciting possibilities, they also pose challenges. The shift to data-driven business models requires security providers to rethink their offerings and adapt to a rapidly changing landscape. By embracing open-platform development and fostering collaboration, companies like i-PRO are setting an example of how the industry can evolve.
As the conversation wrapped up, Sekiguchi reflected on the broader implications of these changes. “The security industry is moving beyond its traditional boundaries. What we do today is only a glimpse of what’s possible in the future.”
The vision shared by Sekiguchi and Hansel paints an ambitious picture of the future. Security solutions will no longer be confined to monitoring and recording; they will be embedded within interconnected systems, contributing to broader societal goals. From autonomous operations to contextual AI, the next 50 years promise a complete reimagining of what security technology can achieve.
For i-PRO, the journey ahead is clear: continue to innovate, collaborate, and push the boundaries of what is possible in the pursuit of a safer, smarter world with an ethical outlook on every aspect.