Reducing false alarms with Deep Learning

April 2018 Editor's Choice, Surveillance

The Deep Learning phenomenon continues to excite the IT world, with computing power now at the level where it can be properly used in practical applications.

Hikvision has been at the forefront of applying the technology in the surveillance industry and beyond, and has already released its first set of products that harness the power of Artificial Intelligence (AI).

The concept of Deep Learning takes inspiration from the way the human brain works. Our brains can be seen as a very complex deep learning model. Brain neural networks are comprised of billions of interconnected neurons; deep learning simulates this structure. These multi-layer networks can collect information and perform corresponding actions according to analysis of that information.

In the past two years, the technology has excelled in speech recognition, computer vision, voice translation, and much more. It has even surpassed human capabilities in the areas of facial verification and image classification; hence, it has been highly regarded in the field of video surveillance for the security industry.

Its ability to enhance the recognition of human beings – distinguishing them from animals, for example – makes the technology a great addition to the security arsenal. This is especially relevant in a world where false alarms account for 94%-99% of all alarms, according to police and fire service statistics!

How Deep Learning works

Deep Learning is intrinsically different from other algorithms. The way it solves the insufficiencies of traditional algorithms is encompassed in the following aspects.

The algorithmic model for Deep Learning has a much deeper structure than the traditional algorithms. Sometimes, the number of layers can reach over a hundred, enabling it to process large amounts of data in complex classifications. Deep Learning is very similar to the human learning process, and has a layer-by-layer feature-abstraction process. Each layer will have different ‘weighting,’ and this weighting reflects on what was learnt about the images’ ‘components.’ The higher the layer level, the more specific the components.

Just like the human brain, an original signal in Deep Learning passes through layers of processing; next, it takes a partial understanding (shallow) to an overall abstraction (deep) where it can perceive the object.

Deep Learning does not require manual intervention but relies on a computer to extract features by itself. This way, it is able to extract as many features from the target as possible, including abstract features that are difficult or impossible to describe. The more features there are, the more accurate the recognition and classification will be. Some of the most direct benefits that Deep Learning algorithms can bring include achieving comparable or even better-than-human pattern recognition accuracy, strong anti-interference capabilities, and the ability to classify and recognise thousands of features.

Challenges of existing systems

Conventional surveillance systems mostly detect moving targets, without further analysis. Even smart IP cameras can only map individual points on a shape one by one, making it difficult to calibrate some features (e.g. forehead or cheek), thus decreasing accuracy.

False alarm filtering.
False alarm filtering.

For perimeter security, for example, other technologies can be (and are) used to provide more comprehensive security. But they all have their downsides. Infrared emission detectors can be ‘jumped over’, but are also prone to false alarms caused by animals. Electronic fences can be a safety hazard, and are limited in certain areas. Some of these solutions can also be expensive and complicated to install.

Objects such as animals, leaves, or even light, can cause false alarms, so being able to identify the presence of a human shape really improves the accuracy of perimeter VCA functions. Frequent false alarms are always an issue for end-users, who need to spend time to investigate each one, potentially delaying any necessary response and generally affecting efficiency.

Imagine, for example, a scenario where it’s relatively quiet – a location at night where there are few cars and people around. Even here, there could be 50 false alarms in a night. Assuming it takes 2 to 3 minutes to check out a false alarm, and that just 3 out of the 50 warrant more attention – say 15 minutes each. A guard either needs to check the system and look back at the alert, or someone needs to be dispatched to the location and look around, checking if anyone has indeed entered without permission. In most organisations, these would need to be reported/recorded too, adding to the overall time spent on this false alarm. So, those 50 false alarms could cost more than two hours each night of wasted time in that scenario.

Deep Learning, however, makes a big difference. With a large amount of good quality data from the cameras and other sources, like the Hikvision Research Institute, and over a hundred data cleaning team members to label the video images, sample data with millions of categories within the industry have been accumulated. With this large amount of quality training data, human, vehicle, and object pattern recognition models become more and more accurate for video surveillance use.

Based on a series of experiments, the recognition accuracy of solutions using the Deep Learning algorithm increased accuracy by 38% – applying this to the previous example, that’s a saving of nearly one hour each night. This makes Deep Learning technology a great advantage in a perimeter security solution, with much more accurate line crossing, intrusion, entrance and exit detection.

Other uses

The value of Deep Learning technology stretches further than traditional security. For example, tracking movement patterns of individuals can see if they are loitering and a potential threat in the future. A threshold could be set to a five-metre radius of movement, or ten seconds of staying in the same place. If the person passes either threshold, an alarm could be triggered. The solution tracks the individual and compares this behaviour to a database to see if it recognises a pattern.

Another application would be in a scenario where falling down could be a threat, like an elderly care home. If a height threshold was set at 0,5 m and duration time 10 seconds, for example, the solution would be able to see a person falling down (as they go below 0,5 m) and might be in trouble (if they stay down for longer than 10 seconds). The solution uses the parameters set to compare with its database and raise an alarm.

With features and benefits like these, it’s easy to see how many smart applications could be catered for by Deep Learning technology.

Hikvision’s 10 000-strong R&D Centre is pushing the boundaries of surveillance solutions and bringing even more benefits to the market. Artificial Intelligence has massive potential, and Hikvision is always exploring new ways to apply this exciting technology throughout the security industry and beyond.

Deeper intelligence. Deeper surveillance

Hikvision Deep Learning solutions are available at three levels:

1. DeepinView cameras can conduct target tracking, grading and capturing when an alarm is triggered.

2. Traditional IP cameras using a DeepinMind NVR will add the function of searching intelligently by picture, saving time on searching for targets compared with a regular NVR.

3. DeepinView cameras and DeepinMind NVRs deliver a full power solution, with the camera sending the information to the NVR, which can then analyse it. This accelerates recording and false alarm filtering.

For more information contact Janis Roux, Hikvision South Africa, +27 (0)10 035 1172, [email protected], www.hikvision.com



Credit(s)




Share this article:
Share via emailShare via LinkedInPrint this page



Further reading:

Identity, Security & Access Alliance focuses on intelligence and integration
SMART Security Solutions Ideco Biometrics BoomGate Systems Bosch Building Technologies Technews Publishing Integrated Solutions Surveillance Access Control & Identity Management
The Identity, Security & Access Alliance (ISAA) hosted several launch events in Johannesburg in August, showcasing the participating companies’ technical solutions with a primary focus on the solutions made possible by integrating high-quality systems to deliver comprehensive solutions.

Read more...
Make BIG and COMPLEX small and manageable
neaMetrics Suprema AI & Data Analytics Surveillance Integrated Solutions
Traditional CCTV and access systems often operate separately, creating gaps in visibility and efficiency. TRASSIR and Suprema have partnered to develop an integrated platform that improves security, operations, and situational awareness.

Read more...
Get the AI fundamentals right
Technews Publishing SMART Security Solutions Leaderware Editor's Choice Surveillance AI & Data Analytics
Much of the marketing for CCTV AI detection implies the client can just drop the AI into their existing systems and operations, and they will be detecting all criminals and be far more efficient when doing it.

Read more...
SMART Surveillance Conference in Johannesburg
Arteco Global Africa Technews Publishing SMART Security Solutions Axis Communications SA neaMetrics Editor's Choice Surveillance Security Services & Risk Management Logistics (Industry) AI & Data Analytics
SMART Security Solutions hosted its annual SMART Surveillance Conference in Johannesburg in July, welcoming several guests, sponsors, and speakers for an informative and enjoyable day examining the evolution of the surveillance market.

Read more...
Securing South Africa’s logistics sector
Secutel Technologies Products & Solutions Surveillance Logistics (Industry)
Unlike traditional guarding services, Visual Verifier operates on an ‘Always On’ principle, ensuring continuous 24/7 coverage of warehouses, depots, transit hubs, and delivery points.

Read more...
Unlock the future of security operations in Bloemfontein
DeepAlert News & Events Surveillance
Security professionals and business leaders are invited to revolutionise their offsite monitoring operations at the DeepAlert Product Road Show, taking place on 16 – 17 September 2025, at the Schoemanspark Golf Club, Bloemfontein.

Read more...
Your Wi-Fi router is about to start watching you
News & Events Surveillance Security Services & Risk Management
Advanced algorithms are able to analyse your Wi-Fi signals and create a representation of your movements, turning your home's Wi-Fi into a motion detection and personal identification system.

Read more...
South African fire standards in a nutshell
Fire & Safety Editor's Choice Training & Education
The importance of compliant fire detection systems and proper fire protection cannot be overstated, especially for businesses. Statistics reveal that 44% of businesses fail to reopen after a fire.

Read more...
LidarVision for substation security
Fire & Safety Government and Parastatal (Industry) Editor's Choice
EG.D supplies electricity to 2,7 million people in the southern regions of the Czech Republic, on the borders of Austria and Germany. The company operates and maintains infrastructure, including power lines and high-voltage transformer substations.

Read more...
Standards for fire detection
Fire & Safety Associations Editor's Choice
In previous articles in the series on fire standards, Nick Collins discussed SANS 10400-T and SANS 10139. In this editorial, he continues with SANS 322 – Fire Detection and Alarm Systems for Hospitals.

Read more...










While every effort has been made to ensure the accuracy of the information contained herein, the publisher and its agents cannot be held responsible for any errors contained, or any loss incurred as a result. Articles published do not necessarily reflect the views of the publishers. The editor reserves the right to alter or cut copy. Articles submitted are deemed to have been cleared for publication. Advertisements and company contact details are published as provided by the advertiser. Technews Publishing (Pty) Ltd cannot be held responsible for the accuracy or veracity of supplied material.




© Technews Publishing (Pty) Ltd. | All Rights Reserved.