What to know about the changing landscape of video surveillance

Video surveillance systems are ever changing. Stay ahead of the changes | Spot AI


Matt Klingbeil



The video surveillance market is crowded, complicated, and confusing. There are countless players and products that all appear to be similar. Now, AI has added another layer of complexity to the video surveillance market, causing organizations to spend more time understanding the role that AI will play in their next camera system.

How did we get here with more options than ever and what’s at stake for IT and security teams when choosing their next video surveillance system? Let’s start by unpacking how we got to this point.


Evolution of the video surveillance system

There have been several technological transformations over since the start of the century. The major shift has been from analog age to a digital age, where cloud capabilities are expected and the power of AI is beginning to be realized.

2000s: The IP camera era

Prior to the 2000s, analog cameras and VHS tapes were the primary methods to recording. However, quality was limited and tapes had to constantly be replaced. Then, in the 2000s there was a shift to DVRs, which allowed for improved recording, resolution, storage, and flexibility. IP cameras allowed video data to be transmitted over a network, and NVRs allowed even more flexibility with remote monitoring and efficient storage solutions.

Despite technological advancements, NVRs presented technical challenges. While many software applications transitioned to the cloud, IP cameras lagged behind and users were only able to access their video footage on-site or through a VPN. This put tremendous pressure on IT teams for system management.

2010s: The cloud era

In the 2010s, cloud-based video surveillance emerged, allowing businesses to store footage remotely and provide accessibility from anywhere with an internet connection. Two new types of solution hit the market - cloud cameras and cloud NVRs.

Cloud cameras and cloud camera systems

Vendors like Verkada and Rhombus brought cloud cameras and cloud camera systems with a cloud-managed architecture that combined recording, storage, and processing on the cameras themselves, eliminating the need for DVRs and NVRs. This type of cloud-managed architecture provided benefits over the traditional DVR vs. NVR solutions, offering remote access, automatic software updates, and a modern user interface. However, these cloud camera solutions required customers to rip and replace their existing cameras with proprietary cameras, as well as large-scale upgrades to their IT infrastructure.

Cloud NVR

Vendors like Eagle Eye led the Cloud NVR category, with an architecture that allowed buyers to keep their existing cameras while still gaining the benefits of a single pane of glass. These solutions operate on the edge by connecting an NVR to the internet to store and process video data locally and in the cloud.

The approach provided customers with the security, bandwidth, and cost benefits of an on-prem NVR, and the flexibility and convenience of the cloud. An NVR is still required, but these solutions seamlessly enable a single pane of glass for all cameras across multiple locations. This mitigates upfront costs associated with the full replacement of cameras.

Today, cloud capabilities are the expectation.

2020s: The AI era

Every piece of software will get reinvented with AI, and camera systems are no exception. The AI Camera System is the future of camera systems.

GPU compute power has drastically increased in the years since - doubling every year. The ability to train large and complex AI models has followed in lockstep. This, along with the emergence of Foundation Models, has led AI capabilities to improve at rates faster than ever before.

Hybrid AI

These rapid improvements have led to the emergence of a new architecture of video surveillance products — known as Hybrid AI. This new architecture leverages an appliance connected to the network called an Intelligent Video Recorder (IVR). These are similar to NVRs, but have GPUs or Tensor Processing Units (TPUs) built in that allow for AI processing at the edge, as well as in the cloud.

A new set of vendors are leveraging Hybrid AI architecture and bringing AI Camera Systems to market. These products offer all the benefits of the cloud as a baseline, but also allows for AI applications that are performant, usable, interoperable, and scalable to accommodate the rapid developments in AI technology.

As AI Camera System models are entering the market, purpose built for AI, existing cloud camera and cloud NVR vendors are attempting to retrofit their offerings with AI. On the surface, the products may appear similar, but it’s important to understand how to cut through the marketing and select a system that meets your business needs.

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