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Head of product & marketing, Hanwha Techwin Europe

May 11, 2020


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Video Analytics

Can false alarms become a thing of the past?

Uri Guterman, Head of Product & Marketing for Hanwha Techwin Europe, alerts us to the potential for Deep Learning video analytics to significantly reduce false alarms.

There was a time when the cost of visually monitoring activity at a remote site was far too high for most businesses. However, the availability in recent years of high speed, low cost broadband and the move to event driven monitoring, has made it possible for security control rooms to offer a remote visual monitoring service at an affordable price.

Event driven monitoring, or exception reporting as it is often referred to, became an accepted method of bringing an incident to a control room operator’s attention when it became apparent that it was inefficient for them to constantly look at a screen just in case something suspicious might occur. This was because apart from the cost of having to employ large numbers of operators to monitor a relatively low number of sites, there was the risk that something might be missed because of a lack of concentration.


Just having to visually verify event driven alerts generated by sensor devices or server based motion detection software, and more recently by edge based video analytics software, means operators can simultaneously monitor a large number of sites.

The false alarms challenge

Despite constant advances in technology, these methods of visually verifying an alarm can be a victim of their own success in that they often generate unwanted false alarms as a consequence of an inability to accurately distinguish between, for example, a stray animal and a human intruder or a vehicle and what may just be video noise. In addition to the time wasted and the cost implications of having to deal with these false alarms, which might involve sending a key holder to a site to verify what may or may not be occurring, they can also be a major cause of frustration to control room operators. This has been a major issue which has led to some control rooms switching off some of the earlier generation of video analytics which were perhaps oversold in terms of their ability to offer a high level of detection without generating a large number of false alarms.

AI based solutions

Hanwha Techwin believes that by harnessing the power of Deep Learning video analytics, it is able to provide businesses, local authorities and other organisations, as well as commercially run security control rooms, with a powerful tool to help them keep one step ahead of intruders. Running onboard high definition cameras, license-free deep learning video analytics offers a high level of detection accuracy, whilst minimising false alarms.

This is because it is able to simultaneously detect and classify various object types, including people, vehicles, faces and license plates. The specific AI cameras manufactured by Hanwha Techwin are supported by Wisenet AI algorithms which are able to identify the attributes of objects or people, such as their age group, their gender or the colour of the clothing a person is wearing.

The attributes are saved as metadata alongside the images captured by the AI cameras, enabling users to quickly search for specific objects or incidents, with the algorithms even able to recognise if a person is wearing glasses or holding a bag.

Most importantly, Deep Learning video analytics can be configured to ignore 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 ability to minimise time wasting and costly false alarms means control room operators and security personnel are able to focus on responding to real incidents and emergencies.

A report produced by Memoori Smart Building Research predicts video surveillance product sales could increase from $19.15Bn in 2019 to $35.82Bn by 2024, and that AI video analytics software is likely to make a significant contribution to this growth. This is perhaps not surprising bearing in mind its potential alone to make remote monitoring more efficient and affordable, as well as help combat criminal activity in so many other ways, such as providing the ability to quickly and forensically search recorded video for evidence of any incidents. In this respect, within the not too distant future, we will see the introduction of AI applications developed by Hanwha Techwin and its technology partners which will enhance video surveillance systems to a level which at this time might not seem possible.

Do you have some questions about AI? Email Uri Guterman at [email protected]

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