In today’s data-driven world, Video Analytics are a pivotal tool for transforming raw video footage into actionable insights. This technology is instrumental in various sectors, from enhancing security with recognition-based systems, to real-time traffic monitoring, to maintaining workplace safety. By extracting value from visual data, these analytics open new avenues for operational efficiency and decision-making.

However, there are challenges to managing the computer resources required for video analytics. High latency, bandwidth limitations, and security concerns are common hurdles in processing and analysing large amounts of video data. In order to overcome these issues, many organisations are turning to Edge Computing.

Common Issues With Video Analytics Infrastructure

In order to function, video analytics applications require a robust network infrastructure. This is due to their massive data requirements, as large volumes of data need to be analysed and processed. This analysis is highly bandwidth-intensive, and any latency issues common in public cloud based solutions can greatly bottleneck the process. When exploring an on-prem computing solution, performance is not an issue— however, cost and equipment management becomes a concern.

Here’s a breakdown of the common issues faced by these solution options:

On-Premise

On-premise infrastructure has a high set up and management cost. This Capital Expenditure (CAPEX) can be a significant barrier for initial setup, and organisations have to invest heavily to utilise it. On top of this upfront cost, facility maintenance and ongoing hardware maintenance bring even larger financial commitments. Technology that reaches its End Of Life (EOL) period will require updates and refreshments, and a dedicated IT team will be required to manage the wide variety of equipment. This intense level of commitment prevents it from being effortlessly scalable, and it can become a cumbersome and resource-intensive operation.

Public Cloud

Transitioning to Public Cloud services for video analytics may alleviate some of the challenges that on-premise equipment brings, but a major issue is application latency. The distance between the data source and the cloud processing units can introduce a delay that’s detrimental to real-time analytics performance. Even though public cloud services offer better scalability compared to on-premise alternatives, the latency issue remains a concern for organisations that require immediate data processing and analysis.

With both On-Premise and Public Cloud equipment having their own range of inadequacies, it becomes clear that a more efficient video analytics solution is needed, in order to help organisations meet their requirements. That’s where Edge Cloud Computing comes in.

How Edge Computing Can Improve Your Video Analytics

Edge Computing is a game-changer for video analytics. The main concept behind Edge Computing is to process data and services as close to the end-user as possible. Unlike traditional cloud computing, which relies on a centralised data-processing location, edge computing processes data either on the device itself, or on a local computer or server, rather than in a distant data centre. This approach is particularly beneficial in scenarios where real-time processing and rapid data analysis are critical, such as in video analytics, where sending data back and forth to a distant cloud can result in latency issues.

By bringing data processing closer to the source of data generation, video analytics can reduce latency and conserve network bandwidth, greatly improving performance. This eliminates the need to constantly rely on cloud-based solutions or invest heavily in on-premise infrastructure, thus decreasing the starting capital required for a video analytics setup.

From On-Premise to Edge Cloud

Making the switch from on-premise to edge cloud video analytics can help you achieve:

• Lower Total Cost of Ownership (TCO): By eliminating the need for costly upfront investment in infrastructure, edge computing helps save resources and lower the TCO of a video analytics algorithm.
• Reduced IT Footprint: Edge computing diminishes the IT footprint, which is beneficial as it requires less space for computerised devices or software programs.
• Higher Scalability: Unlike on-premise solutions, which are often limited by physical constraints, edge cloud infrastructure is more easily scalable, supporting business growth without substantial physical expansions.

From Public Cloud to Edge Cloud

The primary benefit of using an edge cloud over a publicly available one is:

• Latency Improvement: A cloud solution may not give you the best application performance as the computer resources may not be close to your data source (or even in the country at all). With an edge cloud solution, you’ll benefit from improved latency with resources that are closer to the things, events and people that you serve.
• Data Sovreignty: Gain peace of mind that your data will not leave Singapore’s shores.

Invest in Edge-based Video Analytics

Quick and efficient video analytics solutions can be achieved by leveraging our Edge Cloud Computing. Our Edge Cloud is securely housed within a Critical Infrastructure Information (CII) facility, ensuring unparalleled security and reliability. Supported by business class digital network that delivers <1ms latency island-wide, it’s ideal for real-time data processing and analytics in today’s fast-paced business environment.

We have also recently enhanced our edge offering with Infrastructure-as-a-Service capabilities as well as a hybrid cloud management platform. To find out more about, check out our recent press release here.

To find out more about our Edge Cloud Computing solutions, contact us for enquiries, and let our team of experts assist you in taking your analytics to the next level.