Why Deep Learning can revolutionise video surveillance

Issue 4 2020 CCTV, Surveillance & Remote Monitoring

As technology takes over more aspects of our personal and professional lives, companies are increasingly turning to automation to streamline simple, manual tasks and in turn, implementing advanced technology such as artificial intelligence to offload decision-making processes. The aim, of course, being to help them save the time and cost associated with having staff carry out these activities manually.

As technology takes over more aspects of our personal and professional lives, companies are increasingly turning to automation to streamline simple, manual tasks and in turn, implementing advanced technology such as artificial intelligence to offload decision-making processes. The aim, of course, being to help them save the time and cost associated with having staff carry out these activities manually.

This scenario is particularly apt in light of the current COVID-19 crisis compelling most workers to stay in their homes. With business operations hindered and productivity jeopardised, technologies like automation and artificial intelligence obviously spring to mind as potential solutions.

In the long-term, a situation where human workers aren’t needed and machines take care of everything could be appealing. However, this is simply not an option when it comes to security. While AI can provide security staff with invaluable help, eliminating human decision-making completely could cause serious incidents – and with just the technology to blame, when accidents do happen, this may put a hard stop to the exciting growth of AI in security and the business benefits it can bring. That’s why the key to an efficient, accurate and cost effective video surveillance strategy is a combination of sophisticated technology and human interpretation.

Let’s take a look at how this synergy of artificial intelligence and human input makes for a winning security approach.

Deep Learning for enhanced accuracy

Video analytics undoubtedly revolutionised video surveillance. The shift from a situation where a staff member had to constantly monitor security footage to spot potential intruders, to one where the system itself was able to alert users of suspicious behaviour was game-changing. Before, if an intrusion was missed, resulting in a break-in or damage, the footage had to be watched retrospectively to identify the offenders, with very little scope for success. Now, however, security operators are able to act in real time, while the incident is taking place, increasing their chances of preventing crimes or containing their consequences.

However, monitoring systems, just like individuals, can make mistakes. Tasking technology with flagging potential security threats meant that not all the alerts created were real alarms and they would often call security managers’ attention when there wasn’t a real need. False alarms not only waste monitoring staff’s time, hindering operational efficiency, but also impact their ability to identify anomalous events as they cause a kind of video blindness, making them almost jaded to the excessive number of alerts raised by the system.


Kevin Waterhouse.

It’s a problem of accuracy which, luckily, can be circumvented with the help of a subset of AI – Deep Learning. While video analytics powers the detection of events, Deep Learning can play a key role in enhancing the precision of such detection. A Deep Learning filter can be used to pre-train the system to only flag the presence or movement of humans and vehicles – events, in short, that can represent a security threat. This means wildlife or environmental conditions – which are the main causes of false alarms – would not trigger an alert. This is particularly useful for perimeter protection in sterile zones, where human footfall is very limited and elements like tree branches blowing in the wind or heavy rain could simulate an alarm in a conventional system and alert staff of a potential intruder – causing them to interrupt more important activities. When the alert is generated, that’s where human intervention is incredibly valuable. The AI-powered video analytics may spot an intruder, but it’s still the worker’s responsibility to review the situation brought to their attention by the system and determine how to respond to it.

As it often happens with technology, the hype around artificial intelligence is causing organisations across all fields to consider its implementation, fearing getting left behind and wanting to innovate the way they do business. It’s important, however, to evaluate how such technologies can improve their operations, before rushing to invest in them. When it comes to security and specifically, video surveillance, Deep Learning can provide unprecedented accuracy, minimising false alarms and ultimately, helping security managers and their teams protect premises like never before.




Share this article:
Share via emailShare via LinkedInPrint this page



Further reading:

AI Box for retail
Retail (Industry) CCTV, Surveillance & Remote Monitoring Products
IDIS AI Box for retail delivers advanced video intelligence, even for smaller stores. The simple add-on gives retailers powerful business insights without the price tag.

Read more...
The supervisor role in control rooms
Leaderware Editor's Choice CCTV, Surveillance & Remote Monitoring
The control room supervisor role is not a simple task of making sure that all the staff are present and appear to be performing their duties.

Read more...
Secutel’s workforce management platform
Secutel Technologies CCTV, Surveillance & Remote Monitoring
In the modern day and age, it is quite challenging managing our own lives let alone managing a team of critical workforce guards responsible for the safety of customers and their assets. However, with ...

Read more...
Cybersecuring surveillance devices
HiTek Security Distributors CCTV, Surveillance & Remote Monitoring
Check Point Software partners with Provision-ISR to embed IoT nanotechnology in Provision-ISR’s video surveillance devices for on-device IoT security for video surveillance solutions.

Read more...
Edge or server analytics
XtraVision CCTV, Surveillance & Remote Monitoring
Understanding your requirements and the technology’s capabilities is paramount in making effective decisions as to whether your surveillance system required edge or server analytics, or a combination of both.

Read more...
XVR-I3 series to broaden AI applications
Dahua Technology South Africa CCTV, Surveillance & Remote Monitoring
Dahua recently unveiled an addition to its AI-enabled XVR series by releasing the XVR-I3 models, supporting an array of AI features including full-channel SMD Plus and AI Coding, Perimeter Protection and more.

Read more...
Self-learning AI for existing CCTV systems
Iris AI Editor's Choice CCTV, Surveillance & Remote Monitoring News
Snap Guard is a cloud application that integrates into a property owner’s live CCTV feed, working with existing hardware and software, adding an additional layer of security.

Read more...
Mark Kane and Wayne Schneeberger join Stallion Security
Stallion Security Editor's Choice CCTV, Surveillance & Remote Monitoring Integrated Solutions
Stallion Security has announced that Mark Kane and Wayne Schneeberger have joined its ranks at the same time as the company confirms its acquisition of Myertal Tactical Security’s offsite monitoring business.

Read more...
The Complete Manual on CCTV Management
Technews Publishing Editor's Choice CCTV, Surveillance & Remote Monitoring Security Services & Risk Management
Sonja de Klerk, retired Brigadier from the SAPS Forensic Science Laboratory has written a book on managing your CCTV systems to optimise the value of it as evidence.

Read more...
Perimeter and fire protection
Hikvision South Africa CCTV, Surveillance & Remote Monitoring Fire & Safety Perimeter Security, Alarms & Intruder Detection
Hikvision’s HeatPro is a new series of thermal cameras designed to provide affordable all-weather deterrent and alerts, aimed at perimeter protection and fire prevention applications.

Read more...