How the edge is paving the way for new video surveillance solutions

Head of Product & Marketing for Hanwha Techwin Europe, Uri Guterman, discusses the arrival of ‘the Edge’ and why it provides a pathway to the introduction of new, exciting, innovative and sustainable video surveillance solutions.

Previously, when the only CCTV available was analogue-based, an individual would have needed a locally based cassette recorder (VCR) in order to retain the video evidence captured by security cameras. This meant having to buy a large number of video tapes to ensure images could be stored for up to 30 days or longer. Organisations with multiple sites could also choose to remotely monitor images transmitted via PSTN, ASDL or expensive dedicated leased lines to a central control room.

With the arrival of IP network based video surveillance, users were given the option of conveniently storing large amounts of video data onto a network video recorder (NVR) or server. A major benefit of this was that there was no longer a need to clog up the network by transmitting video footage of scenes where nothing important was happening. As a result, event driven monitoring, which is also known as exception reporting, became an accepted method of bringing an incident to a control room operators’ attention. Operators could also quickly and easily retrieve pre and post event images of any incident.

The benefit of this became even more apparent with the introduction of multi-pixel cameras, when there was a need for the captured video data to share the available bandwidth with whatever else was being transmitted around the network.

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READ: Edge-based video surveillance: The pros and cons

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Although most large video surveillance systems installed in recent years are likely to be using network resources which are isolated from a company’s main network infrastructure, the opportunity to minimise bandwidth requirements and the associated costs, continues to be a major benefit.

Processing power

Cameras with dual SD slots, which collectively offer up to 512GB storage capacity, can provide security personnel with peace of mind in knowing that what may be crucial evidence will be stored safely at the Edge. At full frame rate, the images continually captured by 2-megapixel high definition cameras, for example, will be stored for up to approximately 20 days before they are overwritten. The number of days recorded can be increased if a lower frame rate is acceptable and/or if a manufacturer has developed its own compression technology which complements H.265, minimising storage requirements and improving bandwidth efficiency.

SD cards also help keep data safe at the Edge in the event of network disruption. The deployment of cameras featuring Auto Recovery Backup (ARB) will ensure that activity recorded on an SD card if a connection is temporarily lost, will be automatically transmitted to a remote recording device when the connection is restored.

Edge-based applications

Video surveillance cameras are increasingly being regarded as smart IT devices, equipped with an imaging sensor and a lens. The capabilities of cameras now that they have the ability to run on-board video analytic applications has increased greatly. These include heat mapping, people counting and queue management, as well as pandemic related face mask detection, social distance measuring and occupancy monitoring solutions. With the processing power to do so, mining captured information at the Edge avoids transmitting masses of data across the network.

Whilst manufacturers such as Hanwha Techwin are supplying cameras pre-loaded with these applications, as well as Intelligent Video Analytics (IVA) such as tampering, directional detection, defocus detection, virtual line, enter/exit and motion detection, the Edge provides the perfect opportunity for specialist third-party software developers to innovate by way of developing ground-breaking, serverless solutions that meet the specific needs of individual vertical market sectors.

Cost saving and scalable solutions

As an example, there is a serverless ‘small site’ ANPR solution available which automatically controls the movement of whitelisted cars through barriers via camera relay outputs. It also provides valuable car park management information, such as ‘time spent’ and occupancy rates. It does so without users having to incur the cost of installing and running the application on a server, as up to 4 cameras (1 master camera and 3 slaves) are able to simultaneously capture and transmit video analytics data to a convenient user interface.

Serverless edge-based solutions are scalable and thus provide users with the flexibility to incrementally expand their systems at any time, without having to procure an expensive server.

Deep Learning AI video analytics

These applications elevate video surveillance from just being a security system which helps monitor and detect suspicious activity, to a smart solution which delivers so much more.

The recent introduction of affordable cameras supplied with Deep Learning AI video analytics onboard, has further enhanced the ability of cameras to be used as detection devices. Deep Learning AI video analytics ignores video noise, waving trees, moving clouds and animals, all of which might normally be the cause of false alarms when standard motion detection technology or sensors are being used to detect activity.

This higher level of performance means control room operators and security personnel can focus on responding to real incidents instead of false alarms. In addition to extreme accuracy, deep learning also allows operators to search for specific features and attributes, including a person’s age group and gender, and whether they are wearing glasses, a hat, or carrying a bag.

Retail and traffic applications

Cameras equipped with Deep Learning AI are particularly suitable for applications which require a greater degree of sophistication than that offered by traditional video analytics. It enables, for example, retailers to capture business intelligence such as age and gender and analyse customer demographics. In doing so, these retailers can gain a greater understanding of customer behaviour and buying patterns.

Road planners, traffic regulation enforcement authorities and police are also now able to take advantage of AI cameras to identify the make, model and colour of vehicles, in addition to recognising car number plates. The shortly to be launched edge-based Wisenet Road AI intelligent traffic management solution will, as an example, utilise Deep Learning AI video analytics to identify over 700 vehicle models manufactured across 70 brands. The data can be used to conduct surveys and gain a greater understanding of road usage, as well as help accurately identify vehicles involved in traffic infringements.

Summary

System integrators will find the Edge provides them with the opportunity to offer their end-user clients innovative and sustainable systems, such as self-contained smart devices which could even perhaps be powered by solar panels. In doing so, they will be able to capitalise on the latest technology to deliver solutions which would not have been thought possible back when low resolution, monochrome CCTV images had to be expensively transmitted and remotely monitored from a central location.

More information is available here.