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AI has opened doors to many transformation opportunities and increasingly minimised many risks -- personal and economic -- that are alarming today. And illicit trade is one of those pains AI can offer a promising solution against. Illicit trade is a serious threat and problem that affects governments and societies on every level. While governments lose financial funds in tax revenues, thriving businesses are losing potential customers, and customers are getting tricked into purchasing counterfeit, low-quality products. Transnational organized crime generates revenue of $2.2 trillion through transnational criminal organizations, complicit corrupt facilitators, and other threat areas. The list of criminal activities is long and involves such horrific crimes as trafficking of narcotics, opioids, arms, humans, fake medicines and other counterfeit and pirated goods; illegal tobacco and alcohol; illegally-harvested timber, wildlife, and fish; pillaged oil, diamonds, gold, and other natural resources and precious minerals; stolen antiquities; and other contraband or valuable items sold across streets, social media, online marketplaces, and the dark web. In short, illicit trade is a contributing cause to large-scale insecurity and instability across markets. AI-driven technologies Here is where revolutionary AI-driven technologies come in, with their capability to fight illicit trade across markets. AI technologies in this specific application promise to help build safer and more secure communities in the future. There are a few ways that AI can support the ongoing fight against illicit trade on a global scale in a tangible way. Transnational organized crime generates revenue of $2.2 trillion For financial transactions at risk of fraud and money laundering, tracking has become an increasing headache if done manually. As a solution to this labour-intensive process, AI technology can be trained to follow all the compliance rules and process a large number of documents -- often billions of pages of documents -- in a short period of time. Among these documents widely in circulation, most have an unstructured and inconsistent format -- from invoices to insurance documentation -- are a complex system to tackle; in this whirlwind of pages, the likelihood of non-compliant and misrepresented figures to go by unnoticed. But this is also where AI can thrive as solutions become a necessity, enhancing humans’ capabilities of identifying fraud risks in the early stages. Relying on natural language processing, the technology can begin interpreting the text from the scanned and digitised documents in order to process trade information at high speed. In this context, AI-powered solutions are capable of comparing, contrasting document information and identify anomalies worth looking into further. By automating a large portion of the process, AI-driven technology allows the staff to focus on more pressing, high-involvement issues that require human judgement while saving time on the time-consuming manual work of analyzing documents by hand. Identifying covert interactions between criminals As criminal networks become increasingly intricate in their illegal operations, cutting-edge AI technology is crucial in the battle against it. In 2019, EU launched a project with the goal to deploy advanced AI technology and robots to identify smuggling across the borders of Portugal, Estonia, Italy, Greece, Bulgaria, Romania and Hungary. With the help of AI, it will be possible to send drones and autonomous vehicles to the most dubious border areas in order to enforce border control and detect smugglers quickly and efficiently. Illicit trade triggers a chain reaction of negative impact across industries, governments and individuals The issues the EU is trying to address through the project include drug and weapon smuggling, as well as human trafficking. From tight border control tracking crossings to communication monitoring to identify covert interactions between criminals on both sides of the border, the project is one among many that will expand the scope of solutions to the illicit trade problems today. AI’s contribution is significant on many levels: from predicting crime and threats to safeguarding public health and safety (in face of human trafficking, counterfeit medicines, and toxic products), these are simply inklings of major shifts that AI technology promises for the future of the battle against illicit trade. So why is fighting illicit trade so crucial? Here are a few reasons why AI’s role in fighting this global issue is crucial. In a world where customers run the risk of being deceived and tricked into buying counterfeit products, companies who provide the original products suffer through the loss of revenue and market share. The same companies are also robbed of their intellectual property and proprietary data. Furthermore, people who are employed in these companies run the risk of losing their jobs as a result of revenue and market share loss. And finally, the last straw is the loss of brand integrity and reputation to the need to compete with low-quality, sometimes even dangerous counterfeit products. Just like a domino effect, illicit trade triggers a chain reaction of negative impact across industries, governments and individuals. And it’s time for AI to step in and stop it.
Smart security is advancing rapidly. As AI and 4K rise in adoption on smart video cameras, these higher video resolutions are driving the demand for more data to be stored on-camera. AI and smart video promise to extract greater insights from security video. Complex, extensive camera networks will already require a large amount of data storage, particularly if this is 24/7 monitoring from smart video-enabled devices. With 4K-compliant cameras projected to make up over 24% of all network cameras shipped by 2023 – there is a fast-growing desire for reliable storage on-board security cameras. The question for businesses is: do they look to break up their existing smart video network, by separating and compartmentalising cameras to handle data requirements, or do they increase its storage capabilities? As some people begin to venture out and return to work following initial COVID-19 measures, we are also seeing demand for thermal imaging technology increase. New technology like this combined with more of these always-on systems being rolled out, means organizations will need to carefully consider their smart video strategy. Newer edge computing will play an important role in capturing, collecting, and analyzing data and there are some key trends you can expect to see as a result of this evolution. There are many more types of cameras being used today, such as body cameras, dashboard cameras, and new Internet of Things (IoT) devices and sensors. Video data is so rich nowadays, you can analyze it and deduce a lot of valuable information in real-time, instead of post-event. Edge computing and smart security As public cloud adoption grew, companies and organizations saw the platform as a centralized location for big data. However, recently there’s been opposition to that trend. Instead we are now seeing data processed at the edge, rather than in the cloud. There is one main reason for this change in preference: latency. Newer edge computing will play an important role in capturing, collecting, and analyzing data Latency is an important consideration when trying to carry out real-time pattern recognition. It’s very difficult for cameras to process data – 4K surveillance video recorded 24/7 – if it has to go back to a centralized data center hundreds of miles away. This data analysis needs to happen quickly in order to be timely and applicable to dynamic situations, such as public safety. By storing relevant data at the edge, AI inferencing can happen much faster. Doing so can lead to safer communities, more effective operations, and smarter infrastructure. UHD and storage AI-enabled applications and capabilities, such as pattern recognition, depend on high-definition resolutions such as 4K – also known as Ultra High Definition (UHD). This detailed data has a major impact on storage – both the capacity and speeds at which it needs to be written, and the network. Compared to HD, 4K video has much higher storage requirements and we even have 8K on the horizon. As we know, 4K video has four times the number of pixels as HD video. In addition, 4K compliant video supports 8, 10, and 12 bits per channel that translate to 24-, 30- or 36-bit color depth per pixel. A similar pattern holds for HD — more color using 24 bits or less color using 10 or 12 bits in color depth per pixel. Altogether, there is up to a 5.7x increase in bits generated by 4K vs. 1080 pixel video. Larger video files place new demands on data infrastructure for both video production and surveillance. Which means investing in data infrastructure becomes a key consideration when looking into smart security. Always-on connectivity Whether designing solutions that have limited connectivity or ultra-fast 5G capabilities, most smart security solutions need to operate 24/7, regardless of their environment. Yet, on occasion, the underlying hardware and software systems fail. In the event of this, it is important to establish a failover process to ensure continued operation or restore data after a failure, including everything from traffic control to sensors to camera feeds and more. Consider the example of a hospital with dozens or even over a hundred cameras connected to a centralized recorder via IP. If the Ethernet goes down, no video can be captured. Such an event could pose a serious threat to the safety and security of hospital patients and staff. For this reason, microSD cards are used in cameras to enable continuous recording. Software tools – powered by AI – can then “patch” missing data streams with the content captured on the card to ensure the video stream can be viewed chronologically with no content gaps. Thermal imaging Health and safety is the number one priority for all organizations as people return to work and public spaces. Some organizations are deploying thermal imaging to help screen individuals for symptoms as they return. Organizations that operate with warehouses, depots and assembly lines will traditionally have large amounts of cameras located outside of the entrance. With thermal imaging smart video in place, these cameras can now serve a dual purpose as a screening device. The thermal imaging technology is capable of detecting elevated body temperatures, with 10-25 workers being scanned in one shot, from one camera – making it an efficient and accurate process. This way, staff can use the information to help identify people who may need further screening, testing, and/or isolation before returning to work. There are many more types of cameras being used today, such as body cameras, dashboard cameras, and new Internet of Things (IoT) devices While this may not increase data storage requirements, it can change your retention policies and practices. Smart security today is about utilising AI and edge computing, to deliver an always-on, high-resolution video provision that can help keep people safe 24/7. These trends increase the demands and importance of monitoring, which means requirements of the supporting data infrastructure improve to match that, including the ability to proactively manage the infrastructure to help ensure reliable operation. Companies need to make sure they have considered all the storage and policy challenges as part of their smart security strategy for the future.
Stadiums around the world are still paralyzed from the effects of COVID-19. Fans and spectators in masses have been absent from stadiums since April and there doesn’t seem to be a concrete plan on how or when they’ll be able to return to near capacity. The NBA recently opted to form a bubble philosophy concept in Disney’s facilities, although it’s been a relative success, it’s also been a $200 million temporary solution. This then begs the question: How long can stadiums survive like this without spectator’s present? History tells us that stadiums, venues and sport recover from disasters, so what can stadiums do to speed up the process? This is the catalyst for AI to be integrated on mass level to stadiums around the world. AI is the answer AI’s role in getting fans and spectators back is huge, through capabilities such as: Social Distance Monitoring Crowd Scanning/Metrics Facial Recognition Fever Detection Track & Trace Providing Behavioural Analytics Technologies such as IREX.ai is now working alongside National Leagues, Franchises and Governing Bodies to implement AI surveillance software into their CCTV/surveillance cameras. This is now creating a more collaborative effort from the operations team in stadiums, rather than purely security. Stadiums around the world are still paralyzed from the effects of COVID-19 AI surveillance software such as IREX.ai when implemented into the surveillance cameras can be accessed by designated users on any device and on any browser platform. Crowd metrics Arming stadiums with AI-powered surveillance tools can detect crowd metrics such as “people counting” and “group statistics”. This ensures stadium personnel can monitor social distancing with precision, accuracy and immediately. Alerts can be set up throughout parts of the stadium to alert senior staff members when overcrowding can appear with real time videos, analytics and photos to their hand-held device, such as a smartphone. Fever detection Thermal cameras have been implemented throughout facilities including stadiums and are helping assist to spot people with elevated temperatures. What IREX.ai implements is an alert system, coupled with facial recognition of any individual(s) that read an elevated body temperature. This alert system then provides security and health officials with a photo of the individual with the elevated body temperature, meaning staff can react quicker to the situation prevent this individual from entry. Pandemic monitoring by facial recognition Thermal cameras have been implemented throughout facilities including stadiums and are helping assist to spot people with elevated temperatures Through facial recognition, staff members will be able to locate individuals through simply uploading a photo. It has never been easier to find a person of interest. With masks becoming an everyday part of society, facial recognition has come under scrutiny regarding the accuracy when a mask is worn. Irex.ai still maintains a 96% accuracy with individuals wearing masks and can set up alerts for any individuals not wearing a mask. Another important aspect of facial recognition is finding persons of interest quickly through technology like IREX.ai’s “searchveillance”. The future is here. Designated staff can track a person from when they enter the stadium by simply uploading their photograph. An example of how this can assist stadium personnel is to help relocate lost children inside the stadium with their guardians/parents when they are separated. Another attribute would be any individuals banned from entering the stadium would trigger alerts once they appear under surveillance, a fantastic collaborative tool to use with Law Enforcement. Return on investment With security solutions, one of the biggest issues with any security investment is a lack of an ROI. This is where AI security is breaking the mould. The ability to provide business analytics, consumer/fan behaviours, traffic patterns, etc, allows other departments within the organization to gain vital information that can assist with their strategies and practices. Stadium security will never be the same in a post-COVID world, so why will its practices stay the same? AI & Stadiums is no longer the future, it’s the 2020 solution.
LILIN, a globally renowned manufacturer of IP video cameras, recording devices, and software, announced a strategic partnership with CyberLink, a pioneer of AI and facial recognition technologies to strike business opportunities for facial recognition. Together, CyberLink’s FaceMe AI facial recognition engine will be integrated into the LILIN facial recognition system to create comprehensive smart security and smart retail solutions. FaceMe AI facial recognition solution CyberLink’s FaceMe AI facial recognition engine achieves up to 99.7% accuracy of the NIST FRVT test, ranked No. 1 in Taiwan, and when combined with LILIN’s smart security solutions, enables one-stop smart security, data analysis and warning system service. LILIN’s smart security solution is suitable for access control management of commercial offices and factories and can provide business analysis for the restaurant and retail industries as well. AI facial recognition system LILIN’s AI facial recognition system not only has the advantage of ‘3 High’ elements, encompassing a ‘High’ recognition rate Along with over 40 years of experience in IP video manufacturing and development of IP cameras, recording devices and software, LILIN continues to pursue innovation. LILIN’s AI facial recognition system not only has the advantage of ‘3 High’ elements, encompassing a ‘High’ recognition rate, ‘High’ capacity, up to 100,000 face database and ‘High’ elevation angle to be recognized, but also supports multiple image inputs, and image search facial recognition, making it easy to distinguish the trajectory of personnel. Furthermore, companies can create file identification through a photo of the ID, and use one-stop central management to synchronize the data immediately, greatly reducing the precious time spent on complicated processes! Intelligent security solutions “LILIN has many years of smart security experience, providing insight into the market’s needs for creating a comprehensive intelligent security solution. LILIN is pleased to partner with CyberLink and integrates FaceMe into our facial recognition system to strengthen smart retail, smart healthcare, smart factory, and smart business applications. Through continued efforts, I believe that LILIN will provide the most advanced total security solution for global customers.” said Mr. C.C. Hsu, LILIN’s President. “If there was ever a field worthy of continued research and innovation, it’s security,” said Dr. Jau Huang, CEO of CyberLink, adding “Without a doubt, LILIN is a globally renowned provider and manufacturer of IoT devices and CyberLink is a worldwide pioneer developing facial recognition applications for connected devices. Together, we are setting a new standard for what makes a place secure by bringing to market new technologies that make our customers safer, and our businesses smarter.”
The COVID-19 pandemic is the defining global health crisis of our time. In order to be able to fight against it, mask detection and temperature measurement have become daily routines for everyone. In this situation, Merit LILIN, with over 40 years of experience in IP video manufacturers of IP cameras, recording devices, and software, have created a cost-effective COVID-19 solution to ensure end-user health and safety. Temperature measurement Many solutions should provide temperature information, with alert settings that trigger alarms when temperatures are high. The main component of LILIN Temperature Measuring Camera is a dual-lens camera with two sensors, one a visible image lens and the other a thermal array sensor. This design not only provides temperature information and color image information but also high-temperature alert settings when the temperature exceeds. Mask detection With COVID-19 rules and regulations now incorporating person protection equipment such as masks, many solutions are now geared towards mask detection. LILIN has its own mask detection AI, which can be run on an i3 CPU-supported PC. Using the LILIN AI system and Aida NAV server, the system can recognize if the person is wearing a mask or not. If the system identifies a user that is not wearing a mask, an alert goes out with an image of the person. It allows the application to run automatically and enforces the wearing of masks in many situations. A notification is sent to an administrator when an image of a person is captured. Social distancing measures In addition to temperature measurement and mask detection becoming daily epidemic prevention matters, maintaining social distance is extremely significant as well. Through the LILIN Aida detection system, people can calculate the social density and use it with alarm notifications, if an area exceeds the density threshold. Moreover, the LILIN COVID-19 solution has a wide range of applications in different sectors, including schools, public transportation systems, offices, retail and more. By doing so, extensive safety measures can combat COVID-19, providing the public with intelligent epidemic prevention solutions, through innovative AI detection systems.
With the introduction of the AIDA Traffic Management platform, LILIN has transformed Intelligent Traffic Systems from expensive, often inaccurate, and inflexible computer-vision solutions into a new simplified platform with embedded Artificial Intelligence (AI) that learns, adapts and detects every detail of street-level vehicle activity. Engineered for seamless integration into LILIN Windows®-based VMS products including Navigator Control Center, Navigator Corporate, and Navigator Enterprise, AIDA Traffic Management installs on the edge and uses metadata to bring real-time traffic data to organizations and municipalities. Enforcing parking and traffic violations AIDA is purpose-built for a flexible range of ITS applications, making each user-selected function completely automated and cost-efficient. For example, AIDA can extract data from vehicles traveling on multi-lane highways or urban streets, allowing municipalities to better enforce parking and traffic violations, recover stolen cars, or assist law enforcement in Amber Alerts. AIDA can recognize 12 license plates per second on vehicles traveling up to 125 miles per hour AIDA can recognize 12 license plates per second on vehicles traveling up to 125 miles per hour, casting an astonishingly wide net. Police can store and later access this data, enabling investigators to zero in on a suspect's whereabouts and behavioral patterns. Besides monitoring cars, trucks and motorcycles, AIDA can detect pedestrians loitering or entering restricted areas. Capturing vital operational data In a parking lot, AIDA can compare license plate data to pre-defined lists of allowed or excluded vehicles before taking an appropriate action, such as opening a gate or generating an alert if an unauthorized vehicle enters an area as a value-added service. It can also locate available parking slots and broadcast this information to incoming cars. In addition to its security role, AIDA can capture vital operational data about parking infrastructures, including the number of cars served by time, day, and length of stay. This data can then be applied to help optimize a staffing schedule and maximize the profitability of a parking lot's rate structure. AIDA removes the hassles of traditional computer vision techniques that require extensive, CPU-draining image processing. In contrast, AIDA software incorporates an AI engine that is instructed to learn what to look for and is able to run multiple models on distributed AI CPU, GPU, and VPUs.
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