Taking AI to the Edge

Nearly every organization has modernization at the top of their priority lists, but not every company has the resources to transform itself overnight. The surveillance industry is no exception. The emergence of technologies like Artificial Intelligence (AI) is reshaping how surveillance systems are configured and deployed, leaving many security professionals to choose the path that makes the most sense for their long-term success.

One method widely embraced by the industry is Edge AI, which many companies are using as an effective method for the way security and surveillance data is gathered, managed, processed, and stored for efficient use. Edge computing involves processing data closer to the source, with the potential to reduce latency and alleviate bandwidth constraints, among many other benefits.

Edge AI refers to running AI models on devices at the “edge” of a network, such as surveillance cameras, allowing centralized management of more functions that are carried out on the camera itself.

Edge AI can be a long-term strategy on its own, enabling a company to migrate to AI at their own pace. Edge AI can allow for easier deployment, as a company’s existing camera infrastructure can be used without the need to replace every camera all at once or invest in expensive servers. The users still benefit from full AI functionality and performance without the potentially burdensome maintenance costs associated with total camera replacement or VMS licensing – while at the same time getting the most use of their current technology expenditures.

It's a similar transition strategy to what the industry saw with the migration from analog to IP, when many companies used encoders to “convert” existing cameras, ultimately getting more lifespan out of current infrastructure without having to do a forklift upgrade all at once.

Edge AI can also be an efficient cloud alternative. In an Edge AI model, even though the majority of data is stored in the cloud, only the most frequently used data is distributed over a network to the user’s fingertips. This significantly reduces storage requirements for individual devices.

The key benefits of Edge AI also include lower long-term operating costs by avoiding ongoing cloud service fees, as well as increased security and privacy benefits by keeping sensitive data processing on-premises and on the local network.

Rapid advancements in edge computing intelligence and capabilities have also paralleled the maturation of cloud technologies, to the point where edge computing is now capable of performing tasks that, in the past, only the cloud could handle.

It’s all about options. Some organizations will prefer to minimize network bandwidth and lower total costs by configuring an edge-based surveillance system. Others prefer a centralized cloud-based operation. Still others may opt for a hybrid approach, giving them the best of both worlds. In fact, many companies, including Hanwha, view this as an ideal ecosystem where edge and cloud solutions co-exist, with neither approach being “better,” and instead simply offering different sets of features and benefits.