June 16, 2020 - securitylinkindia

AI for the Security Industry: Real-World Applications

MassMediaLink India LLP SecurityLink IndiaJune 16, 2020November 28, 2024022 mins

In recent years, Artificial Intelligence (AI) has been the buzzword in the video analytics domain. Trade show stands are rife with AI demos promoting ambitious functionality set to change the face of CCTV in security. Impressive as many of these demonstrations are, there is a definite air of scepticism on the part of the end-user. Is the hype around AI warranted, and can science actually deliver? This feels reminiscent of a decade ago when video analytics promised to revolutionise CCTV monitoring. Today, reliable and effective analytics is the mainstream and is driving tangible business value. That said, there is no denying that the last five years of AI innovation has led to tangible and practical solutions, with the security industry finally starting to reap the benefits. However, AI is now at a precipice – on the cusp of what industry experts call an ‘AI winter’ – so, everyone is wondering what’s next and what is possible. This paper investigates precisely this, focusing on the physical security space. What is AI? One formal definition of Artificial Intelligence (AI) identifies the technology with the “development of computer systems able to perform tasks normally requiring human intelligence such as visual perception, speech recognition, decision-making, and translation between languages.” In reality, the term AI covers a wide range of applications and tends to refer to the current problem being tackled, which of course is constantly evolving. When we think of AI in the security industry, this usually translates to a few key areas: Asset protection & monitoring. Access control. Business intelligence. Decision support. Machine Learning is the process of teaching a system to perform a task, while Deep Learning is just a subset of Machine Learning. There are many other non-deep learning based ML methods which, for the purposes of this paper, will be referred to as traditional ML approaches. Often, when AI is mentioned, what is really being referenced is the Machine Learning (ML) or Deep Learning (DL) algorithm powering that solution. For example, license plate recognition (LPR) is often the application of a DL model to locate and extract a license plate from an image, coupled with ML algorithms cross-referencing information from a database. Therefore, this application should be referred to as a combination of ML and DL – not simply AI. The distinction between traditional ML and DL is an important one, as the recent boom in AI solutions often refers to advances in Deep Learning techniques. In the majority of cases, the use of Deep Learning has led to a significant jump in accuracy over traditional ML techniques. For example, a well-known academic image classification challenge, in which images must be classified into one of a thousand different classes, has seen a notable increase in accuracy – going from 50% of the images being classified correctly in 2011, using traditional ML techniques, to nearly 90% today using modern DL techniques. The figure below illustrates the improvement in the ImageNet challenge over time. Machine Learning vs Deep Learning To understand Deep Learning’s dramatic improvement over traditional Machine Learning techniques, let’s look at how an example asset protection use case could be approached with both methodologies. The goal is to detect if the object in the field of view of a particular camera represents a threat and should generate an alarm (person, vehicle etc), or constitutes mere background noise that can be ignored. To begin, through the use of a movement-based tracker (another ML system) a camera has detected motion and defined a region of interest around the object. Machine Learning (ML) The traditional Machine Learning pipeline generally requires the developer to represent an input (e.g., a region of interest in an image) into a structured feature descriptor of that input: for example, a set of numbers that represents the shape in the image (HOG, SIFT), or possibly another property in the image (colour, texture etc). The model is then trained by feeding labelled examples of the object feature descriptors you want to recognise (person, vehicle) and object feature descriptors of objects you expect to see but want to ignore (trees, shadows, animals etc.). The Machine Learning algorithm learns to group these feature descriptors into these categories so, when a new unlabelled feature representation is fed to the system, it can make an assessment as to which category it might fall into. A system’s accuracy hinges on a developers’ ability to come up with a feature descriptor which the Machine Learning algorithm can easily group into classes to detect vs those to ignore. One of the biggest advantages of using human-designed feature descriptors is the data required to train the ML model is reduced. Creation of labelled datasets to train any Machine Learning algorithm takes significant time and therefore resource. As a consequence, traditional Machine Learning techniques are still very much relevant due to this significant time and cost-saving. Deep Learning (DL) Deep Learning follows a similar process. However, instead of relying on a human-in-the-loop method of developing a robust feature descriptor, the Deep Learning system itself just looks at the labelled input data to learn the best way of grouping the images. By showing the system large numbers of samples (training), the system refines its model to best describe the data it is being shown. The disadvantage is that, for a Deep Learning model to learn that best representation from the data, a notably larger amount of data is necessary. However, although the data requirements are more significant, the Deep Learning approach removes the guesswork of a developer trying to define the optimal representation of an input to enable the system to learn. It also has the advantage that the same approach can be applicable to a range of different problems, whereas traditional ML may require redesigning the feature descriptor based on the application. Deep Learning has demonstrated its advantages over traditional methods. However, the real question is how it can be used to improve business processes or increase precision in detection, while reducing costs for security businesses….

Feature

Transcending the Norm in Cash-in-Transit

MassMediaLink India LLP SecurityLink IndiaJune 16, 2020November 14, 202408 mins

In a world of constantly evolving economy, robbery, theft and other property crimes are also rapidly increasing. The need for businesses and organizations to outsource their banking transactions and have professional couriers handle their cash logistics becomes a necessity. While it’s true that cash management can be expensive, it also entails managing risks for both the couriers and the clients. For some cash-in-transit (CIT) companies, the use of uniformed and armed CIT professionals is deemed necessary. Others even utilize armoured trucks or armoured transport services. These vehicles are mostly bulletproof and are fashioned to transport extremely large quantities of money, ATM replenishment, and transport dignitaries or VIPs. However, not all cash-in-transit companies employ uniformed couriers and armoured vehicles. Why are covert operations better? Since the main obligation of any cash-in-transit company is to collect and deliver cash or a client’s valuables to the bank or any designated point, some companies find the use of armoured vehicle services essential for the business. But how safe is their hard-earned money in the hands of these couriers? According to a publication from the Australian Institute of Criminology, a total of 89 robbery incidents were recorded by Australian CIT companies over a 20-year period (1989-2008); specifically, there were 18 incidents in 2007 and 11 incidents in 2008. In the book entitled Encyclopedia of Victimology and Crime Prevention by Bonnie S. Fisher and Steven P. Lab, it was mentioned that even though most robbers target banks or any financial institutions, others still prefer robbing cash-in-transit vehicles. The robbers depict two main methods in committing the crime: (1) stopping and then attacking the CIT vehicle or (2) robbing the driver and couriers during delivery or after cash collection. As the leading cash-in-transit company in Australia, SecureCash focuses on covert operations. While using armoured trucks may be the norm in the security service industry, SecureCash has always operated in unmarked, soft-skinned vehicles and have never utilized armoured transports as part of its CIT fleet. From a security standpoint, a covert operation is a safe and risk-free approach to delivering an efficient cash-in-transit service. It eliminates risks During the transfer of cash or other valuables, the business becomes exposed and vulnerable to risks such as robberies. Try to imagine a big armoured vehicle or an armoured truck pulling up outside the office or home. This will certainly catch the eyes of criminals, from would-be thieves to organized crime syndicates. These criminals may decide not to rob the armoured truck or attack the couriers, but rather choose to plot a robbery right at the business location or home. By not using armoured vehicles, it will not only keep the couriers and drivers away from possible threats, but it will also ensure that the valuables and the client’s business stay safe. Aside from discouraging the use of armoured vehicle services, the bank couriers do not wear security uniforms or any marked, printed, easy-to-spot clothing. This is to avoid drawing too much public attention during the cash handling process. It enhances security SecureCash couriers are trained to master the art of blending into the crowd. In this way, they will be much harder to spot by anyone who’s plotting a robbery or theft. Since they are not easily recognised, their cash transfer patterns are unpredictable and more difficult to analyze. Likewise, an armed security guard or uniformed personnel coming in and out of the office, collecting and carrying an obvious bag of cash indicates a green signal for those prying criminal minds who are just waiting for the right opportunity. It’s the same as advertising to the public that a person has huge amounts of cash on-site to warrant the service of cash couriers. This is what Secure Cash has been trying to prevent, so clients will remain dedicated and keep the trust that they have given to the company for the last 25 years. Bethaney Bacchus, General Manager of SecureCash, shared that specializing in covert cash handling operations has provided many growth-oriented opportunities for the company and helped build quite a reputation among clients and partners nationwide. She added, “We save time for busy people by performing the banking duties, we offer a secure service by taking the risk away from people who are fearful to carry cash in public, we offer convenient service for people who suddenly find their bank branch has closed.” Managing cash and other valuables may be an expensive and risky venture, however, implementing tried and tested innovative solutions prove to be the key to a successful business operation. With the emergence of many security service providers in Australia, the effectiveness of one’s services provides the business with an extensive competitive advantage. To be a market leader in the industry, one must be the person who goes beyond the norm.