Articles by Mai Truong
As the multi-billion-dollar market for artificial-intelligence-based video analytics continues to grow, so does the number of video analytics solution providers. In Q3 of 2018, Stockholm-based consulting company Memoori identified 128 active companies in the supply chain for AI video analytics [i]. This list is far from exhaustive, considering how analytics has been gaining interest and becoming mainstream in 2020, with users expecting more accurate alerts based on object detection instead of motion detection, hardware providers developing more powerful but compact chip sets for deployment, and more startup solution providers carving out their niche in the market. Given so many choices, the question arises as to how a system integrator can evaluate and select the best solution for his customer. Although the criteria vary for each vertical, there are some key metrics to consider across the field: Open platform Ease of use Robustness and reliable performance Versatility Good support and integration Low total cost of ownership 1. Open platform Open platform allows the user to have complete flexibility, avoid being locked into any particular manufacturer, and utilise the best-of-breed solution available in each category. Analytics has been gaining interest and becoming mainstream in 2020 In 2019, an IPVM survey shows that 51% of system integrators always prefer an open platform to an end-to-end solution (i.e., all components including camera, VMS, analytics, etc. provided by one manufacturer), and 24% select open platform or end-to-end depending on customer requirements [ii]. For analytics, as the users commonly have an existing infrastructure, investing in a technology overhaul would be too expensive. An open-platform analytics product, i.e., a camera-agnostic, VMS-agnostic, and computer-server-agnostic product, will add value to the existing infrastructure within a reasonable budget. Open platform also makes it easier and more cost-efficient to upgrade each component when necessary. 2. Ease of use One of the main reasons and goals of applying AI to security is to help the user automate the process of watching hours and hours of surveillance videos, extract useful information and send alerts when necessary. In other words, AI should make it easier for the user to operate the security system. Thus, a good AI video analytics solution must be easy to set up and connect to the existing infrastructure, easy to use on a daily basis, and easy to scale with the expansion of the business. Let us examine each point in more details: Easy to set up: a turn-key, plug-and-play solution helps save time and money. The system integrator can spend a couple of hours instead of days to help the customer set up. In both 2018 and 2020, the most common reason that integrators cited for choosing a solution is that “it just works” [iii][iv]. Easy to use: an intuitive, no-learning-curve user interface allows the customer to make the solution second-nature, maximize its utility and gets the highest return on investment. The best-case scenario is that everyone in the user’s organisation, e.g., every police officer in a city police department, can use the solution on a daily basis, not limited to a technical staff with rigorous training. Easy to scale: the solution must be designed to seamlessly scale in different ways: number of cameras (e.g., from a few to a few thousands); deployment locations (e.g., can we access data in our branch office in another city? how about another country?); types of device and deployment (e.g., body-worn cameras, in-vehicle, control center, cloud). 3. Robustness and reliable performance Traditional VMD (video motion detection) -based analytics have many limitations and false alarms, so AI-based analytics were developed, primarily to identify different objects in the videos with high accuracy. However, such accuracy must be achievable in different real-life environments. The best solution does not let low lighting, snow and rain, spider crawling in front of the cameras, etc., interfere with human intrusion detection or license plate recognition at night. In the case of temperature detection, users should be able to walk by the system at a normal pace without removing the mask to minimise disruption and maximise worker efficiency. A more robust solution means less time and resource spent on false alarms. 4. Versatility A versatile, feature-rich, multi-functionality video analytics is the most effective choice for system integrators in the long term. Not limited to only object detection, AI can be trained to recognise higher levels of details (e.g., faces, age, gender, license plates), track objects (including people and vehicles), and detect certain behaviours (e.g., loitering, theft). In other words, a more versatile analytics solution can recognise more types and behaviors of objects for more use cases. Most users have certain pain points today and are looking for only one or a few solutions. However, as the organisation grows, new situations and requirements may arise, which call for new detection functions in video analytics. The costs and complexity will add up quickly if each solution has only one function. A few examples: An LPR camera may be perfect for the need to record all license plates today, but if the police wants to find a black Toyota Prius with “A23” in the plate number, a solution that can detect the plate number, vehicle make and model will save much more time and effort. Intrusion detection based on the ability to distinguish human from other moving objects (e.g., animals) is only the first step. What if the user needs an alert for people that enter a construction zone without a hard hat and safety vest? The answer is an AI solution that can grow its repertoire. In the current pandemic, business must adopt temperature screening, distancing detection, occupancy detection, and mask detection; a solution that can provide all four analytics in one platform is clearly more useful than four individual solutions, not to mention whether the solution can be repurposed after the pandemic has been resolved. 5. Good support and good integration One of the main reasons that system integrators might select an end-to-end solution instead of an open-platform one is technical support: more responsiveness and less finger-pointing. In terms of responsiveness, good technical support is a part of the ease of use, where the system integrator and the user can rest assured that any question can be answer via email or a phone call to the manufacturer. A more robust solution means less time and resource spent on false alarms In terms of having a one-stop-shop solution to reduce finger-pointing, good support means the manufacturer can provide easy integration to 3rd-party systems, which includes API interface support. One example is access control. Video analytics is a great tool to enhance access security (e.g., face recognition to open doors for employees; LPR for parking management; weapon detection linked to automatic locked-down system), but only 24% of video surveillance systems today are integrated with access control [v]. Two of the main reasons: (1) integration is expensive, and (2) the systems are not compatible. Both hurdles can be overcome if the analytics solution bridges the gap between cameras and access control system via its API. 6. Low total cost of ownership These six criteria help both the system integrator and the end-user save time, money, and effort Cost is always a determining factor, especially in the SMB market [vi]. Customers’ expectations are high, and higher-resolution cameras are decreasing in price and increasing in numbers, which means more data to process than ever. A good analytic software solution is not only capable of many functions, its algorithms are efficient enough to fit more into the same server specs, and it does not require expensive cameras to have good accuracy, thereby increasing cost saving for the entire system. In summary, these six criteria help both the system integrator and the end-user save time, money, and effort and get the most out of video analytics in the long run. A high-performance, versatile, turnkey solution is already a reality with today’s technology, and it will only continue to improve, so there is no reason to settle for less. [i] Memoori, The Global Market for Intelligent Video Analytics 2018 to 2023, 2018 [ii] IPVM, Open vs. End-to-End System: Statistics 2019, November 11, 2019 [iii] John Honovich, IPVM, Favorite Video Analytic Manufacturers 2018, April 2, 2018 [iv] IPVM, Favorite Video Analytic Manufacturers 2020, February 25, 2020 [v] Brian Rhodes, IPVM, Access Control and Video Integration Statistics 2020, October 8, 2020. [vi] Brian Karas, IPVM, Low Cost, Low End Competitors Challenge SMB Surveillance Market, September 1, 2017
Our Expert Panel Roundtable is an opinionated group. However, for a variety of reasons, we are sometimes guilty of not publishing their musings in a timely manner. At the end of 2020, we came across several interesting comments among those that were previously unpublished. Following is a catch-all collection of those responses, addressing some of the most current and important issues in the security marketplace in 2021.
News reports and opinion columns about face recognition are appearing everyday. To some of us, the term sounds overly intrusive. It even makes people shrink back into their seats or shake their head in disgust, picturing a present-day dystopia. Yet to others, face recognition presents technology-enabled realistic opportunities to fight, and win, the battle against crime. What are the facts about face recognition? Which side is right? Well, there is no definitive answer because, as with all powerful tools, it all depends on who uses it. Face recognition can, in fact, be used in an immoral or controversial manner. But, it can also be immensely beneficial in providing a safe and secure atmosphere for those in its presence. Concerns of facial recognition With the increased facial recognition applications, people’s concerns over the technology continuously appear throughout news channels and social media. Some of the concerns include: Privacy: Alex Perry of Mashable sums up his and most other peoples’ privacy concerns with face recognition technology when he wrote, “The first and most obvious reason why people are unhappy about facial recognition is that it's unpleasant by nature. Increasing government surveillance has been a hot-button issue for many, many years, and tech like Amazon's Rekognition software is only making the dystopian future feel even more real”. Accuracy: People are worried about the possibilities of inaccurate face detection, which could result in wrongful identification or criminalisation. Awareness: Face recognition software allows the user to upload a picture of anyone, regardless of whether that person knows of it. An article posted on The Conversation states, “There is a lack of detailed and specific information as to how facial recognition is actually used. This means that we are not given the opportunity to consent to the recording, analysing and storing of our images in databases. By denying us the opportunity to consent, we are denied choice and control over the use of our own images” Debunking concerns The concerns with privacy, accuracy, and awareness are all legitimate and valid concerns. However, let us look at the facts and examine the reasons why face recognition, like any other technology, can be responsibly used: Privacy concerns: Unlike the fictional dystopian future where every action, even in one’s own home, is monitored by a centralised authority, the reality is that face recognition technology only helps the security guard monitoring public locations where security cameras are installed. There is fundamentally no difference between a human security guard at the door and an AI-based software in terms of recognising people on watchlist and not recognising those who are not. The only difference is that the AI-based face recognition software can do so at a higher speed and without fatigue. Face recognition software only recognises faces that the user has put in the system, which is not every person on the planet, nor could it ever be. Accuracy concerns: It is true that first-generation face recognition systems have a large margin for error according to studies in 2014. However, as of 2020, the best face recognition systems are now around 99.8% accurate. New AI models are continuously being trained with larger, more relevant, more diverse and less biased datasets. The error margin found in face recognition software today is comparable to that of a person, and it will continue to decrease as we better understand the limitations, train increasingly better AI and deploy AI in more suitable settings. Awareness concerns: While not entirely comforting, the fact is that we are often being watched one way or another on a security camera. Informa showed that in 2014, 245 million cameras were active worldwide, this number jumped to 656 million in 2018 and is projected to nearly double in 2021. Security camera systems, like security guards, are local business and government’s precaution measures to minimise incidents such as shoplifting, car thefts, vandalism and violence. In other words, visitors to locations with security systems have tacitly agreed to the monitoring in exchange for using the service provided by those locations in safety, and visitors are indeed aware of the existence of security cameras. Face recognition software is only another layer of security, and anyone who is not a security threat is unlikely to be registered in the system without explicit consent. The benefits In August 2019, the NYPD used face recognition software to catch a rapist within 24 hours after the incident occurred. In April 2019, the Sichuan Provincial Public Security Department in China, found a 13-year-old girl using face recognition technology. The girl had gone missing in 2009, persuading many people that she would never be found again. Face recognition presents technology-enabled realistic opportunities to fight, and win, the battle against crimeIn the UK, the face recognition system helps Welsh police forces with the detection and prevention of crime. "For police it can help facilitate the identification process and it can reduce it to minutes and seconds," says Alexeis Garcia-Perez, a researcher on cybersecurity management at Coventry University. "They can identify someone in a short amount of time and in doing that they can minimise false arrests and other issues that the public will not see in a very positive way". In fact, nearly 60% Americans polled in 2019 accept the use of face recognition by law enforcement to enhance public safety. Forbes magazine states that “When people know they are being watched, they are less likely to commit crimes so the possibility of facial recognition technology being used could deter crime”. Saving time One thing that all AI functions have been proven to achieve better results than manual security is speed. NBC News writes, “Nearly instantaneously, the program gives a list of potential matches loaded with information that can help him confirm the identity of the people he’s stopped - and whether they have any outstanding warrants. Previously, he’d have to let the person go or bring them in to be fingerprinted”. Facial recognition can also be immensely beneficial in providing a safe and secure atmosphere for those in its presence With AI, instead of spending hours or days to sift through terabytes of video data, the security staff can locate a suspect within seconds. This time-saving benefit is essential to the overall security of any institution, for in most security threat situations, time is of the utmost importance. Another way in which the technology saves time is its ability to enable employees (but not visitors) to open doors to their office in real time with no badge, alleviating the bottleneck of forgotten badge, keycode or password. Saving money A truly high-performance AI software helps save money in many ways. First, if the face recognition software works with your pre-existing camera system, there is no need to replace cameras, hence saving cost on infrastructure. Second, AI alleviates much of the required manual security monitoring 24/7, as the technology will detect people of interest and automatically and timely alert the authorities. Third, by enhancing access authentication, employees save time and can maximise productivity in more important processes. The takeaway AI-enabled face recognition technology has a lot of benefits if used correctly. Can it be abused? Yes, like all tools that mankind has made from antiquity. Should it be deployed? The evidence indicates that the many benefits of this complex feature outweigh the small chance for abuse of power. It is not only a step in the right direction for the security industry but also for the overall impact on daily lives. It helps to make the world a safer place.
On November 2019 in Stockton, California, surveillance footage found that vandals shot out glass windows and doors in many places in a small business complex (FOX40). The intruders broke in only to leave with nothing, proving their intent was solely to vandalize the property. Meanwhile, it was reported that a trio of ATM thieves struck around 9 times across many different locations inside Brooklyn and Queens within just over a month in fall 2019 (ATM Marketplace). On average, the cost of vandalism to SMB is around $3,370 per incident (US Small Business Administration), including a staggering 692 vehicle vandalism claims per day. Likewise, the average cost of theft to SMB is about $300 per shoplifting incident and $1,500 per employee theft incident, which accounts for 38% and 34.5% of all theft instances, respectively (National Retail Security Survey). High-performance artificial intelligent systems can automate the monitoring tasks Vandalism and theft have proven time and time again to be inconvenient and deconstructively harmful towards SMB. However, these financial burdens can be prevented with the use of the right security system. AI-based security systems with Deep Learning contain many features that many SMB owners find advantageous in their pursuit to stop unwarranted and unwanted money loss. Intrusion and loitering detection The first of many features that can help with vandalism and theft prevention is Intrusion Detection. High-performance artificial intelligent systems can automate the monitoring tasks for high-risk sites to provide a high level of security and security personnel monitoring efficiency. Traditional intrusion detection systems detect objects based on size and location, but they do not recognise the type of objects. Now, Intrusion Detection (Perimeter Protection) systems with cutting-edge, built-in AI algorithms to recognise a plethora of different object types, can distinguish objects of interest, thus significantly decreases the false-positive intrusion rate. The more advanced AI-based systems, like those we offered at IronYun, enable the users to draw ROIs based on break-in points, areas of high-valuables, and any other preference to where alerts may be beneficial. Similarly, AI Loitering Detection can be used to receive alerts on suspicious activity outside any given store. The loitering time and region of interest are customisable in particular systems, which allows for a range of detection options. Advanced loitering detection software as such can detect and trigger real-time alerts for both people loitering and/or vehicles that are illegally parked in certain areas of interest. A benefit, which only certain advanced systems contain, is the ability to send trigger actions to 3rd-party systems in reaction to receiving an alert of loitering and/or intrusion detection. These trigger actions can be set to contact authorities immediately and/or trigger a scare tactic alarm or announcement to intruder/loiterer. Certain Face Recognition and License Plate Recognition software can record individual people/vehicles Face and license plate recognition In addition to the activity detection solutions, certain Face Recognition and License Plate Recognition software can record individual people/vehicles and use pre-configured lists to identify particular faces or plates that may be of interest, such as those in watchlists. These systems can also enable the users to upload images of faces not in the lists and search for them in the camera recording. For instance, if a person is detected several times loitering outside a store, one may save one of the detection photos into the watchlist, and set up an alert when said face is recognised again outside the building in the future. The alerts will help to deter and prevent vandalism or theft, and notify the authorities to the scene before the troublemaker completes the act. The main attributes of high-performance Face Recognition systems which maximise assistance with vandalism and theft management include: Face match rate > 90% with good camera angles and lighting. Processing multiple streams and multiple faces per image. Live face extraction and matching to databases of thousands of faces within 3 seconds. State-of-the-art AI security software with Deep Learning allows the user to no longer need to install special LPR camerasIf the watchlist individual is wearing a mask or their face is not in view of the camera, their license plate may be a good indicator. If a particular car is detected several times loitering in the parking lot or street outside a store, the user can set the alerts for such car to get notified in the future. With an AI solution like this, common street cameras should be equipped with LPR capabilities. So, state-of-the-art AI security software with Deep Learning allows the user to no longer need to install special LPR cameras. High-performance alert mechanisms A high-performance AI solution, in addition to having high accuracy, should be able to: Easily integrate with 3rd-party systems Work well with all ONVIF IP cameras including infrared and thermal ones (for Intrusion detection) Analyses video streams in real time and trigger alerts within a few seconds Send alerts to multiple VMSs, connect with signalling devices such as loud speakers or flashing lights Send email notifications to security staff and police departments Send notification on mobile device using AI NVR mobile app Maintains a record of all alerts to provide evidence of intrusion and loitering instances for police and insurance agencies. To assist in theft and vandalism prevention, AI-based security systems with deep learning will do all of the tedious work for you. Their low cost and high performance also make them the most accessible security solutions in the market with large return on investment. Stopping crimes is a difficult, ongoing challenge, but with the right AI software, business vendors and police departments can do so with more ease.
According to the reports of not-for-profit organisation Gun Violence Archive, the year 2018 has seen 323 mass shooting incidents as of November 28 in the United States. This number is 346 for the year 2017 and 382 for 2016 (more statistics are available here), with “mass shooting” defined as cases where four or more people are shot or killed in the same time period and location. While definitions of mass shooting vary with organisations in the US, the count of over 300 incidents per year, or about once per day on average, is simply alarming. It raises public safety concerns, ignites debates and protests, which in turn lead to public unrest and potentially more violence, and increases costs for governments from the regional to federal level. Most importantly, the loss of lives demands not only improvement in post-incident handling and investigation, but also new prevention technologies. Gunshot detection solutions AI weapon detection offers a more efficient alternative to prevent active shooting There are several gunshot detection solutions in the security market, commonly used by law enforcement agencies to detect and locate gun fires. These systems function based on acoustic recordings and analyses and often in combination with signals detected by sensors of the optical flash and shockwave when a gun is fired. However, gunshot detection by nature dictates that the law enforcement can only react to a shooting incident that has occurred. With fast action, law enforcement can prevent the incident from escalating, but lives that are lost cannot be recovered. With the development of artificial intelligence in object recognition, AI weapon detection offers a more efficient alternative to prevent active shooting: AI can visually detect guns based on their shapes before they are fired. The AI is trained to recognise firearms in different shapes, sizes, colours, and at different angles in videos, so that the AI weapon detector can be deployed with existing cameras systems, analyse the video feeds, and instantly notify security staff when a gun is spotted. Comparison of the advantages for law enforcement and public security agencies Legacy gunshot detection using sensors AI weapon detection Reactive measure: detect after guns have been fired Proactive measure: detect before guns are fired Time to action: within 1 second Time to action: within 1 second Unable to provide visual data about shooter(s) Can provide data about shooter(s) based on the camera recording: clothing, luggage (backpack, handbag, etc.), facial features, vehicle Unable to track the location of the shooter(s) before and after shooting because of the lack of sound Can track the shooter(s) using AI Person & Vehicle Tracking, AI Face Recognition, and AI License Plate Recognition False detection caused by similar sound such as fireworks and cars backfiring Minimal to no false detection, as AI can distinguish different types of handguns and rifles from normal objects (umbrella, cellphone, etc.) Require physical deployment of gunshot detection sensors Can be used with existing camera systems, do not require special hardware Complicated to deploy, require highly trained professional Easy to deploy as an add-on to existing video surveillance system - Can integrate with gun-shot detection to create a “double knock” audio and video active shooter alert system Gun-shot detection advantages In addition to advantages for law enforcement and public security agencies, this type of visual-based pre-incident detector has three-fold advantages for the public: Save lives by spotting the shooter before the shooting event. Minimise the chaos entailing an incident: panic and chaos caused by a shooting incident often adds to injury, as people run, fall, trample on others… With an AI weapon detector, when a gun is spotted, the system sends an alert to security staff, who can quickly control the situation in an organised manner and apprehend the intending shooter. Can be added as a SaaS (Security as a Service) component to small business and home surveillance systems, e.g., intrusion detection alerts (home invasion incidents with firearms number over 2500 per year nationwide). For a complete active shooter detection system, video-based AI detector can operate in conjunction with gunshot detectors for enhanced security. Traditional X-ray based weapon detection or metal detection entrance systems are complicated and expensive; with AI video technology, active shooter detection system can be cost-effective, and after all, what price tag can one put on a life? Written by Paul Sun and Mai Truong, IronYun
With the ever-growing availability of video data thanks to the low cost of high-resolution video cameras and storage, artificial intelligence (AI) and deep learning analytics now have become a necessity for the physical security industry, including access control and intrusion detection. Minimising human error and false positives are the key motivations for applying AI technologies in the security industry. What is artificial intelligence? Artificial intelligence is the ability of machines to learn from experience using a multi-layer neural network, which mimics the human brain, in order to recognise items and patterns and make decisions without human interference. The human brain is estimated to have 86 billion neurons; in comparison, the newest Nvidia GPU Volta has 21 billion transistors (the equivalence of a neuron), which offers the performance of hundreds of CPUs for deep learning.AI can learn continuously 24 hours per day every day, constantly acquiring, retaining and improving its knowledge In addition, unlike humans, AI can learn continuously 24 hours per day every day, constantly acquiring, retaining and improving its knowledge. With such enormous processing power, machines using Nvidia GPU and similar chips can now distinguish faces, animals, vehicles, languages, parts of speech, etc. Depending on the required complexity, level of details, acceptable error margin, and learning data quality, AI can learn new objects within as fast as a few seconds using Spiking Neural Network (SNN) to a few weeks using Convolution Neural Network (CNN). While both SNN and CNN offer advantages and drawbacks, they outperform tradition security systems without AI in terms of efficiency and accuracy. According to the research reports of MarketsandMarkets, the market size of perimeter intrusion detection systems is projected to increase from 4.12 billion USD in 2016 to 5.82 billion USD in 2021 at a Compound Annual Growth Rate (CAGR) of 7.1%. Meanwhile, the predicted market of AI in security (both cyber security and physical security) will grow from 3.92 billion USD in 2017 to 34.81 billion USD by 2025, i.e., with an impressive CAGR of 31.38%. Legacy perimeter intrusion detection systems Legacy perimeter intrusion detection systems (PIDSs) are typically set up with the following considerations: Geographical conditions: landscape, flora, fauna, climate (sunrise, sunset, weather conditions, etc.), whether there are undulations in the terrain that would block the field of view of cameras Presence or lack of other layers of physical protection or barriers Integration with other systems in the security network: camera, storage, other defensive lines (door, lock, alarm, etc.) Types of alarm triggers and responses System complexity: intrusion detection with various types of sensors, e.g., microwave sensors, radar sensors, vibration sensors, acoustic sensors, etc. Length of deployment Local regulations: privacy protection, whether the cameras/sensors must be visible/hidden/buried, etc., electromagnetic interferences that may affect other systems such as oil rigs or power plants Human involvement: on-site personnel arrangement, human monitoring, human action in response to alarms AI object detection can easily distinguish different types of people and objects Pain points and benefits of AI The conditions listed above correspond to certain requirements of an intrusion detection systems: minimal false alarm, easy setup and maintenance, easy integration, and stable performance.AI by nature is designed to learn, adapt itself and evolve to work in multiple conditions: it should be integrated with existing video recording systems Minimal false alarms: False alarms lead to increased cost and inefficiency but are the main problem of PIDSs without AI technology, where animals, trees, shadows, and weather conditions may trigger the sensors. AI object detection can easily distinguish different types of people and objects, e.g., in a region set up to detect people, a car driving by, a cat walking by, or a person’s shadow will not trigger the alarm. Therefore, the amount of false alarms can be reduced by 70% to orders of magnitude. Easy setup and maintenance: Legacy PIDSs without AI must account for terrain, line of sight of cameras, sensor locations; any changes to the system would require manual effort to recalculate such factors and may disturb other components in the system. In contrast, AI PIDSs enable the system administrator to access the entire system or individual cameras from the control room, configure the region and object of interest in the field of view of cameras within minutes, and adjust with ease as often as necessary. Computing knowledge and even specific security training are not required to set up a secured PIDS with AI because AI PIDS is designed to relieve humans from knowing the inner working of machines. Easy integration with complementary technologies: Legacy PIDS without AI relies on physical technology, which are often proprietary and require complete overhaul of systems to function smoothly. On the other hand, AI by nature is designed to learn, adapt itself and evolve to work in multiple conditions, so AI PIDS is easily integrated with existing video recording (camera) and storage (NVR) systems. AI also eliminates the need for physical wireless or fiber-based sensors; instead, it functions based on the videos captured by cameras. Furthermore, AI enables easy and instantaneous combinations of multiple layers of defense, e.g., automatic triggering of door lock, camera movement focusing and access control as soon as a specified object is detected in the region of interest, all set up with a click of a button. Stable performance and durability: Legacy PIDSs without AI requires complicated setup with multiple components in order to increase detection accuracy. More components mean a higher probability of malfunction in the system, including exposure to damages (e.g., sensors can be destroyed) and delay in detection, while human monitoring is inconsistent due to human fatigue (studies have shown that a person can concentrate in mundane tasks for only up to 20 minutes, and the attention span decreases even more rapidly when humans are faced with multiple items at once, e.g., multiple camera monitoring screens). AI significantly reduces, if not completely eliminates the need for human involvement in the intrusion detection system once it is set up. In addition, AI reduces the risk of system malfunction by simplifying the hardware sensors needed. Minimising human error and false positives are the key motivations for applying AI technologies in the security industry Additional benefits of AI in intrusion detection Artificial Intelligence is undeniably reshaping every business and weaving into every aspect of daily lifeMaximal detection capability: The most advanced AI intrusion detection system today provides an all-in-one solution to distinguish any combination of alarm-triggering criteria beyond perimeter protection. Using AI, the system administrator can configure as many zones with different settings and object of interests as necessary, which include detections for specific colors or attributes (e.g., person not wearing the required uniform or carrying food/drink), numbers and dwell time (e.g., group of more than 5 people loitering), or movements (e.g., cars moving faster than the speed limit). In addition, AI can accurately pinpoint the location of event occurrence by displaying the camera that records the event in near real time, i.e., with few-second delays. Lower security operation cost: By minimising the number of false positives and human involvement while maximising ease of use and stability, AI intrusion detection systems significantly decrease the total cost of ownership. Companies can reduce the large security personnel overhead and cost of complicated and expensive legacy PIDSs systems. McKinsey Global report in June 2017 shows that proactive AI adopters can realize up to 15% increase in profit margin across various industries. Artificial Intelligence is undeniably reshaping every business and weaving into every aspect of daily life. In security, legacy systems are giving way to AI-based systems, and the first enterprises to adopt AI-based systems will soon, if not immediately, benefit from such investment. By Paul Sun, CEO of IronYun, and Mai Truong, Marketing Manager of IronYun
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