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Fast And Accurate, Rapiscan’s CT Scanning Improves Airport Baggage Screening

Larry Anderson
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Related Links
  • Preventing Drone Incidents That Threaten Airports
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  • Security Ecosystems Drive The Demand For Identity And Access Management Solutions

Using computed tomography (CT) security technology for cabin baggage screening at airports can eliminate the need for passengers to remove liquids and laptops from their bags, thus helping to make the security process easier and more efficient. As the latest in checkpoint scanning technology, CT provides 3D images with much more information to improve threat detection capabilities so decisions by the operator can be resolved quickly and more accurately, without the need for manual search. The whole experience is better and safer for everyone.

The use of computed tomography

The Netherlands were the first to introduce CT at the checkpoint and the Transportation Security Administration (TSA) in the United States plans to install 145 units in its airports by the end of the year. Australia is already installing and using CT at some of its airports. The United Kingdom is the first country to mandate use of CT for screening cabin baggage, and it is expected that the EU will also mandate this type of technology in the near future.

It is expected that the EU will also mandate this type of technology in the near future

The process of deploying CT is another level in the evolution of passenger screening and aviation security.  Since 9/11, the traveling public has become increasingly educated about aviation security, says Steve Revell, Senior Director of Aviation CT at Rapiscan Systems. “What passengers expect is courtesy, professionalism and, most of all, to be able to travel safely without fear of harm,” he says. “Each level of security we have seen introduced over the years, I believe, shows commitment from all stakeholders to meet these key deliverables.”

Rapiscan Systems' 920CT

Rapiscan Systems' 3D cabin baggage screening system, 920CT, utilizes patented Dual-Energy technology and a sophisticated algorithm to provide the highest level of automatic detection of highly dense items, as well as automatic detection of explosives based on the most stringent global regulatory requirement. It also has a truck wheel design and contactless power and data transfer, both of which reduce maintenance costs and downtime and make the 920CT more efficient and robust.

Rapiscan's 920CT provides the highest level of automatic detection

Rapiscan’s 920CT is equipped with advanced software and detection algorithms that are designed to be upgradeable. With 3D volumetric imaging, the 920CT is superior to 2D systems as it provides improved visualization of potential threats. It has also been engineered to integrate seamlessly with Rapiscan’s TRS tray return solution to create a more efficient checkpoint experience, for both passengers and staff. 

“As a manufacturer, we strive to provide the very best quality service and equipment we can – working closely with regulators and airports alike to ensure the evolution and expansion of air travel is both collectively understood and fit for purpose,” says Revell.

Portfolios and partnerships

Rapiscan has a wide portfolio of products and capabilities. However, one company cannot do it all. “For this reason, we will always look to best-of-breed technologies that complement our offerings and provide a cohesive solution and, more importantly, long-term partnership,” says Revell. “The partnership deliverable is what drives the future. Our mission is one of an open platform that invites other technologies to work in conjunction with ours and integrate in such a way that can provide meaningful reports and controls systems to our clients, driving core efficiencies and security enhancements.”

Our mission is one of an open platform that invites other technologies to work in conjunction with ours

“The efforts borne by each of the key stakeholders is done for one very important and aligned reason, to ensure to the very best of our abilities that passenger safety is put first, is always put first and is done in the most courteous, professional way possible, without any undue inconvenience,” he adds.

Increasingly, airports are asking for suppliers’ systems to talk to each other. Many airports have security screening equipment from multiple manufacturers and want to put images from these into a single, searchable database, to track missing items, improve productivity and efficiency, or comply with regulations. The Digital Imaging and Communication for Security (DICOS) is an industry standard that defines an interoperable data format for integration of security screening technologies across multiple vendor platforms. It facilitates the development and use of improved security screening technologies and third-party software. Rapiscan’s 920CT supports DICOS, and the company is working with multiple vendors to help create a single platform for airports.

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  • Airport surveillance
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  • Airport security
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Author profile

Larry Anderson
Larry Anderson Editor, SecurityInformed.com & SourceSecurity.com

An experienced journalist and long-time presence in the US security industry, Larry is SecurityInformed.com's eyes and ears in the fast-changing security marketplace, attending industry and corporate events, interviewing security leaders and contributing original editorial content to the site. He leads SecurityInformed's team of dedicated editorial and content professionals, guiding the "editorial roadmap" to ensure the site provides the most relevant content for security professionals.

People mentioned in this article

Steve Revell
Steve Revell Rapiscan Systems
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Video surveillance is commonly associated with security. But in most cases, it's used to record incidents and assist in investigations after the fact rather than prevent undesirable events. Artificial intelligence–powered video analytics is a highly promising trend that fundamentally changes the way things work. Extracting manageable data from a video stream can help recognize risky situations early on, minimizing damage and, ideally, completely avoid emergencies. At the same time, AI significantly expands the areas of application of video surveillance beyond security systems. AI significantly expands the areas of application of video surveillance beyond security systems However, the hype around this new, trendy technology prevents the potential user from choosing quality solutions in a wide variety of products. This often leads to over-expectation, followed by a complete let-down. Can AI-powered video analytics really be the key to a technological breakthrough in video surveillance? We'll take a look at what the technology can do, what it can't, and where it can go from here. Technological breakthrough or just another bubble? It's often said that the video management software (VMS) market is becoming increasingly commoditized and widely available. A lot of products with similar features (or, at least, similar promises from the manufacturer) make it hard to choose. As a result, vendor names and reputations are turning into one of their primary selling points. Manufacturers have two choices available: get wrapped up in a price war and rely on cutting expenses, or offer a product that's truly innovative and revolutionary. Manufacturers have two choices available: get wrapped up in a price war, or offer a product that's truly innovative and revolutionary VMS developers who choose the second route are gravitating towards creating products that use artificial intelligence based on neural networks and deep learning. Emerging two or three years ago, the AI video analytics market is experiencing a boom in growth. This new tech wave has stirred the still, stagnant backwaters of the VMS world and gave small, ambitious developers something to be optimistic about. It seems they now have a chance to emerge as market leaders in the next few years. However, the hype around this popular trend is raising reasonable concerns among experienced security industry professionals. These concerns come from clients looking for a solution to their problems, and from suppliers building a long-term development strategy. This largely resembles another tech bubble, like the one built up around pre-AI video analytics and burst when it became clear that the sensational promises around it were pure marketing hype (and rather unscrupulously so). However, there are a lot of factors that indicate that AI-powered video surveillance systems aren't another bubble. The three factors The first — and the main one — comes from systems already in place on customers' sites. They fulfill the same promises made during the previous bubble by hotheads in a rush to teach the computer to analyze events in real time using a classical algorithmic approach. The second is the fact that this new technology has seen investment from not only software and cloud startups, but also established VMS developers. Even giants like Intel, which has presented a full line of neural network accelerator hardware and a set of software tools that streamlines working with them, specifically in the field of computer vision. This new technology has seen investment from not only software and cloud startups, but also established VMS developers The third factor lies in artificial intelligence's abilities. AI plays chess, drives cars, and works wonders in many other fields. Why shouldn't it be applied to video monitoring and analysis? What AI can do Just what can artificial intelligence do in video surveillance systems at this stage of development? It can't quite analyze a sequence of events and understand the "logic" of what's happening in the cameras' field of view. At least not yet. But it's probable that AI will learn to do this in the next few years. But neural network analytics can already detect, classify, and track objects very well, providing high accuracy even in busy scenes. Artificial intelligence can be used in the real world to: detect smoke and flames for early fire warning at open areas (forest, open warehouse, parking lot, etc.); distinguish people/vehicles from animals and other moving objects, e.g. to protect the perimeter of a nature park from poachers; distinguish a person in a helmet and protective clothing from a person without them to prevent accidents at a dangerous production facility or construction site; count objects of a specific type, e.g. cars in a parking lot, people in the sales floor, wares moving on a conveyor belt, etc. in non-security-related solutions. Those are just a few examples. After training a neural network, it can tackle other, similar tasks, too. Generally, a neural network trained in specific conditions isn't replicable. In other words, it won't work as well under different conditions. On the other hand, developers have learned how to quickly train AI for the needs of a specific project. The most important requirement is having enough video footage. Somewhat apart from that is the use of neural networks in facial and automatic number-plate recognition. This is an example of reproducible neural networks (train once, deploy everywhere), which makes them more appealing commercially. If non-reproducible neural networks have only recently become economically feasible due to the rapid evolution of specialized hardware (aforementioned Intel's product, for example), then the use of AI in facial recognition and ANPR has been well established for a long time. The use of AI in facial recognition and ANPR has been well established for a long time Another kind of AI analytics that we'll explore is behavior analytics. This function, probably more than any other, is bringing video surveillance systems closer to understanding what's happening on camera. Its potential is vast. How Behavior Analytics Works From a technical point of view, behavior analytics combines artificial intelligence with a classic algorithmic approach. A neural network trained on a multitude of scenarios can determine the position of the bodies, heads, and limbs of humans in the camera's field of view. The algorithm outputs an array of data containing descriptions of their poses. Conditions can be set for data to detect a specific pose, such as raised hands, prostrated or crouching persons. Developers can use this to quickly create new detection tools to identify potentially dangerous behavior specified by a government or business client. There's no need for additional training of the neural network. How Behavior Analytics Can Be Deployed Someone crouched down next to an ATM could be a technician, CIT guard, or burglar. Bank security should be notified in any of the cases. A person in shooter position, together with a bank employee or cashier with their hands raised could indicate a robbery. The system can be configured to automatically send alerts with a surveillance snapshot to the police so they can assess the threat and take action if needed. It's vital that the police receive the alert, even if the employee is unable to activate the alarm. In many cases, attention should be directed to a prostrate individual. This could be somebody who needs immediate help, or it could be someone sleeping in an inappropriate public place, for example, a 24/7 ATM space. Behavioral analytics can also be used to ensure workplace safety. For example, tracking whether employees are holding the handrails when using the stairs at a manufacturing facility or a construction site. What Now? Behavior analytics can be deployed wherever your clients' imagination takes them. With this feature, practically any pose that indicates potentially dangerous behavior can be detected. 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Q&A: New Arcules CRO Nigel Waterton Reflects On The Cloud, RMR And Their Impact
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Nigel Waterton recently joined cloud video company Arcules to lead the sales and marketing efforts as Chief Revenue Officer (CRO). He brings to the task the benefit of 22 years of experience building and managing large, high-growth technology organizations. Waterton joins Arcules from Aronson Security Group, an ADT Commercial Company, where he served as Senior Vice President of Corporate Strategy and Development. We caught up with the new CRO to discuss his position and to reflect on how industry changes are impacting integrators and manufacturers. Q: What fresh insights do you bring to Arcules from your previous positions? Waterton: Generally, most manufacturers don’t understand the business model of the integrator. And if they do, their programs don’t necessarily help achieve their goals. Since most manufacturers use integrators to get to the end user, they are often disconnected from truly understanding the customer, their organization’s business and its impact on the value of the security program. In my previous role, I spent most of my time bridging the gap between these two worlds. It gives me a great platform for understanding how to achieve that with Arcules. Q: How is ‘Chief Revenue Officer’ different from your previous jobs? I have the responsibility of driving innovation for the companyWaterton: While the title is different, the ultimate role I’m in isn’t too different from previous roles that I’ve held in my career. I have the responsibility of driving innovation and strategy for the company, as well as serving as a leader for the sales and marketing team and developing a sales and marketing strategy for the company. This position allows me to build on what I’ve learned throughout my career from an end-user and integrator partner perspective and brings that expertise into the fold of this young, fresh, innovative company that’s paving the way for cloud-based innovation in the marketplace. Q: Is there an industry-wide ‘culture clash’ between the IT-centric nature of cloud systems and the physical security market? How can it be managed? Waterton: Adopters from the IT and physical security worlds are a little at odds over the software-as-a-service (SaaS) offerings as a result of a disconnect with how the cloud is defined in both spaces. A lot of people and companies are creating their own notion of what cloud and SaaS mean. And without a common nomenclature in place, there is a lot of confusion among all users. Similarly, there is a clash among integrators around how to monetize the SaaS offering. This gap can be closed through increased awareness, education and the reiteration of how ubiquitous the cloud already is in our everyday lives. Q: From the integrator perspective, what is the impact of a transition to a cloud/SaaS model on how revenue is managed in the increasingly service-oriented security market? Waterton: Transitioning to a cloud/SaaS model shifts the mindset of the integrator significantly, as the focus changes from project-centric to more customer service-based impact. Becoming more service-minded creates a greater awareness of what the client’s needs are on a day-to-day basis and how that can be improved over time. When operating with a per-project focus, it can be difficult to create a more long-term impact on an organization. With a cloud-based, service-oriented model, integrators now have the ability to manage client expectations in real-time, which greatly increases their value proposition. Q: What about from the end user perspective? Waterton: There are so many benefits from the end user perspective, including the ability to remove the process of a large investment in capital expenditures (CapEx) and shift to a more manageable, predictable operational expenditure (OpEx). Not only does this allow organizations to adjust as needs change; it also prevents being locked into a long-term solution that might not be able to move with the speed of the company as it scales. That being said, the main benefit is the ability of SaaS/Cloud services to drive innovation and introduce new features as they’re introduced without additional investment from the end user. Q: What impact does the recurring monthly revenue (RMR) model have on the operations/management/cashflow of a supplier/manufacturer company? Waterton: Traditional manufacturers struggle with the introduction of a SaaS modelTraditional manufacturers struggle with the introduction of a SaaS model for many of the same reasons integrators struggle. They must sell the board and possibly their investors on a new valuation model as well as revenue recognition model. That is constraining their innovation in the market. Oddly enough RMR from a manufacturer’s perspective is very similar to the integrator model in that cash flow is more predictable in nature. An RMR model allows a company to grow strategically and innovate constantly, expanding and adjusting to cater to client needs on a daily basis while also providing the ability to look ahead and ensure we’re meeting the needs communicated to us in the market now and into the future. Q: What will be the biggest challenge of your new position at Arcules (and how will you meet the challenge)? Waterton: One of the biggest challenges we’re seeing — and one that will have a significant impact on my role — is the challenge of market adoption of SaaS/cloud services, as well as the awareness about why cloud is a significant part of the future of the industry. There’s also an opportunity to shift the conversation within Arcules from tech-focused outcomes to becoming practitioners of risk-based outcomes. We have to focus on the risk model for organizations, not technology. If we truly understand the risks to the organization, the tool will become apparent. Answering the questions: Why does a retailer lose product? Why does a facility experience vandalism? We have to understand the sociology of it because that’s how we can address what the service does in the marketplace. Q: Taking the various elements into consideration, what will the ‘physical security industry’ look like five years from now? Waterton: In sum, wildly different. It’s much different than what it was five or even 10 years ago, and with each leap, the industry has moved forward. Products are maturing, bandwidth is improving and the knowledge that we have is exponentially more advanced. 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