The video surveillance market has become increasingly crowded, complex, and challenging to navigate. With a plethora of players offering seemingly similar products, the introduction of AI technology has added another layer of intricacy, leaving organizations struggling to understand how AI will impact their next surveillance system.
How did we arrive at this point, with more options than ever before? And what are the critical decisions facing IT and security teams as they select their next video surveillance solution? Let’s take a closer look at the evolution of video surveillance technology and what it means for the future.
The Evolution of Video Surveillance Technology
The video surveillance industry has undergone several significant technological transformations since the start of the 21st century. The biggest shift has been the move from analog to digital, where cloud technology is now expected, and AI is starting to play a pivotal role.
The 2000s: The Era of IP Cameras
Before the 2000s, analog cameras and VHS tapes were the primary methods of video recording, with poor image quality and the constant need to replace tapes. The introduction of digital video recorders (DVRs) in the early 2000s marked a turning point, significantly improving video quality, storage capacity, and flexibility.
IP cameras soon followed, allowing video data to be transmitted over a network, while network video recorders (NVRs) enabled remote monitoring and more efficient storage solutions. Despite these advances, NVRs posed challenges. While most software applications were moving to the cloud, IP camera systems lagged, forcing users to access video footage on-site or via a VPN. This placed substantial pressure on IT teams for system management and maintenance.
The 2010s: The Cloud Era
The 2010s saw the rise of cloud-based video surveillance, enabling businesses to store footage remotely and access it from anywhere with an internet connection. This era brought two new solutions to the market: cloud cameras and cloud NVRs.
Cloud Cameras and Cloud Camera Systems
Companies like Verkada and Rhombus introduced cloud-managed cameras that integrated recording, storage, and processing directly within the cameras, eliminating the need for traditional DVRs and NVRs. These systems offered numerous benefits over older models, including remote access, automatic software updates, and a more modern user interface. However, these cloud camera systems often required organizations to replace their existing cameras with proprietary ones and invest in significant IT infrastructure upgrades.
Cloud NVRs
Meanwhile, vendors like Eagle Eye led the way with cloud NVR solutions, which allowed businesses to retain their existing cameras while still enjoying cloud-based benefits. These systems operate at the edge, with an NVR connected to the internet, enabling both local and cloud storage and processing. This approach combined the security, bandwidth efficiency, and cost savings of an on-premises NVR with the convenience and flexibility of cloud management.
Cloud NVR solutions allowed customers to manage all their cameras through a single interface without the need for a full-scale camera replacement, significantly reducing upfront costs.
The 2020s: The AI Era
As AI continues to reshape the technology landscape, video surveillance systems are no exception. The future of surveillance lies in AI-driven camera systems.
Over the past several years, GPU computing power has surged, doubling roughly every year, making it possible to train increasingly large and complex AI models. This, along with the development of foundation models, has led to rapid advancements in AI capabilities, which are now being integrated into video surveillance systems.
Hybrid AI
These advancements have given rise to a new video surveillance architecture known as Hybrid AI. This architecture relies on an appliance called an Intelligent Video Recorder (IVR), which is similar to an NVR but equipped with GPUs or Tensor Processing Units (TPUs) for AI processing at the edge as well as in the cloud.
A new wave of vendors is bringing AI camera systems to market, leveraging Hybrid AI architecture. These systems offer the benefits of cloud-based solutions while also enabling advanced AI applications that are scalable, interoperable, and capable of keeping pace with the rapid developments in AI technology.
While AI camera systems are purpose-built for AI, many cloud camera and cloud NVR vendors are trying to retrofit their existing offerings with AI features. Although these products may seem comparable at first glance, it’s crucial to cut through the marketing hype and select a solution that aligns with your organization’s specific needs and goals.