Which analytics processing is best – on server or at the edge?

Wondering whether to install video analytics at the edge or on a server? Explore each option's pros and cons to learn which is the best fit.

Video analytics can be a powerful addition to your security installation. You can detect possible intrusions and camera tampering, as well as foot traffic and the direction of vehicles—and that’s just the start! Video analytics are continuously evolving with new functionalities that will enhance your existing deployment.

A lot can affect your decision if you’re eager to install video analytics in your environment. Knowing what you’re looking to accomplish and what infrastructure is currently on-site should influence which setup to choose.

There are three main system architectures for video analytics deployments—edge, server, and cloud. However, as cloud processing is still costly, edge and server remain the most popular architectures for deployment.

Learn the difference between each option and understand when to consider one versus the other.

 

 

The differences between edge and server-based analytics

Before we dive in, let’s clarify the terms:

Server-based analytics has the software with algorithms to process the input and provide the output for your video analytics applications installed on a server. That means video surveillance streams are sent to and processed on a centralized server, whether located on-site or at a different location.

Edge-based analytics is when the software running the video analytics algorithm is located on your camera or encoder. Since these devices sit at the edge of your network while processing images and creating the metadata, it's called edge-based analytics.

It’s important to clarify that each option is viable and effective. But choosing the best option will depend on your architecture, environment, and goals.

The breakdown: advantages and disadvantages

Server-based analytics

Advantages

If you've chosen a video analytics software and aren't overly impressed with the results, you can simply replace the software. But keep in mind that replacing software might require additional deployment steps such as uninstalling, reinstalling, and configuration, along with the costs of buying a new license or upgrading the hardware if necessary.

The software will typically not depend on the camera type, which gives you plenty of freedom to choose the devices that best suit your application. If you want to upgrade your security system and reduce costs by keeping existing edge computing devices, you can do so. Server-based analytics give you lots of flexibility to change things up as needed.

Servers also provide ample processing power to support the most advanced analytics. That means you can run various types of analytics simultaneously and get results instantly.

Server-based analytics can be easier to set up and use as well. When you deploy a security platform with unified analytics, you can configure the analytics from the same interface as your video management system. That means if you’re running various analytics applications, your users will always have a familiar and consistent experience when overseeing your analytics operations.

A unified analytics deployment can save your team a considerable amount of time for installations with many locations or large campuses with hundreds or thousands of cameras.

Disadvantages

So what are the potential shortcomings of server-based video analytics? First, higher-resolution cameras tend to cost more than budget-friendly options. Also, the higher the resolution, the more bandwidth you’ll need to transfer video from cameras to servers. This can be challenging for sites where there are bandwidth limitations.

Finally, since you need to procure cameras with decent resolution, purchase the server infrastructure, and implement other network components, your costs for this deployment can add up.

Organizations can typically justify these higher costs in larger deployments requiring more advanced analytics. That’s because they’ll see value in the broader range of features available within the software, which helps spike efficiencies. In other words, you’ll get powerful analytic tools that help operators streamline security processes and business operations.

Summary: Pros and cons of server-based analytics 

Pros

• An easy-to-maintain centralized solution
• Software can easily be replaced if you change your mind
• Servers offer high processing power to support advanced analytics
• Consistent experience in overseeing analytics operations
• Saves time if installing hundreds/thousands of cameras

Cons

• Higher-resolution cameras tend to cost more
• Requires more bandwidth the higher the resolution
• Single point of failure

   

 

 

Edge-based analytics

Advantages 

There are many reasons why edge-based analytics can be a great choice. For instance, the software on the camera will usually have access to raw, uncompressed video. This makes the reliability of the analytics relatively high.

These analytics can also be easy to deploy. A manufacturer’s camera portfolio with built-in analytics will typically use the same software, so once you learn to configure one, you can easily configure all.

That’s one of the reasons why installing edge-based analytics can be great for remote locations. These sites are often isolated and only have a few cameras. There’s also limited space for additional equipment, and the conditions can be dirty, too hot, or too cold, which aren’t ideal for expensive servers. Running analytics on your cameras addresses these challenges head-on.

In some situations, whether it’s remote or not, you may also be dealing with bandwidth restrictions. Edge analytics are particularly advantageous in this scenario because all the data and processing happens on the camera. Only a simple event or alarm is transmitted over the network to the control room, which requires minimal bandwidth consumption. 

Disadvantages

At first glance, edge-based analytics may seem like a good fit, but here are some essential things to know. Firstly, cameras with built-in analytics have limited processing power, so usually, they can only run one type of analytics at a time. This limits your ability and may slow your response to urgent situations.

There are also fewer camera options to choose from. Camera manufacturers tend to offer only a small selection of devices with analytics or AI onboard. They can also be pretty expensive, which can dissuade those who want to stick within a specific price range per camera.

If you want some flexibility in deploying various cameras from different vendors, edge-based analytics may not be right for you. That’s because there’s little to no compatibility between each vendor’s analytics software, which complicates everything from management to maintenance. For these reasons, it’s often best to stick with one manufacturer when deploying analytics at the edge.

This may lead to another big issue down the road, though. Should you realize your analytics aren’t working the way you anticipated, or you’re just not happy with the outcome of your analytics deployment, you’ll be forced to rip out and replace all the cameras. Essentially, this option may limit your ability to stay flexible and grow.

Summary: Pros and cons of edge-based analytics

Pros

• Access to raw, uncompressed video for high-quality analytics
• Easy to configure
• Best for remote locations where conditions aren’t great
• Ideal for bandwidth restrictions since data and processing occurs on the camera

Cons

• Limited processing power 
• Fewer camera options to choose from
• Little to no compatibility between each vendor’s analytics software
• Limited flexibility and ability to grow

Bottom line: think long-term

Depending on your environment, your tech stack, and what you’re looking to achieve, it’s important to make an educated, long-term sustainable decision.

If you have a remote location with bandwidth limitations, only a few cameras, and all you want to do is detect intrusions, then edge-based analytics may be the right choice.

On the other hand, if your organization has a few campuses with hundreds of cameras and you want to count people, track vehicle directions, monitor intrusions, and try out other interesting analytics over time, server-based analytics is the better option.

Want to learn more about how video analytics can transform your video into smart, actionable information?