Category: White Paper

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….

This white paper discusses radar technology in security applications, and compares it with other available technologies. It also provides specific information about AXIS D2050-VE Network Radar Detector, its usage and its possibilities What is radar? Radar is a well-established technology for detecting objects. It was developed for military use in the 1940s, but is now widely used in civilian applications, for instance weather forecasts, road traffic monitoring, and collision prevention in aviation and shipping. Using radar technology for detection can reduce the number of false alarms and increase detection efficiency in conditions with poor visibility. AXIS D2050-VE Network Radar Detector is Axis’ first available radar-based motion detector. Owing to its advanced tracking algorithm, it is not only an affordable complement to security cameras, but it can also add valuable features to a surveillance system.   A radar device transmits signals consisting of radio waves, or electromagnetic waves in the radio frequency spectrum. When a radar signal hits an object, the signal is reflected or scattered in many directions. A small portion of the signal may be reflected back to the radar device, where it will be detected by a receiver. The detected signal provides information that can be used to determine the location, size and velocity of the object that was hit. Why use radar in surveillance? Due to its superior detection abilities in darkness or fog, a motion detector based on radar can be a cost-efficient complement to other types of surveillance. Reliable in challenging conditions By nature, radar surveillance is not dependent on visibility. Darkness, fog, or even moderate rainfall does not impair the detection abilities. There are other surveillance technologies that may also work in such conditions, for example thermal cameras equipped with video analytics, or PIRbased (passive infrared) motion detectors. However, surveillance based on radar can be a cost-efficient alternative to both solutions. Radar is easier to use, and more affordable than a thermal camera. Radar can also provide more information, at a longer range, than a PIR motion detector. Decreased false alarms Reducing the number of false alarms, while maintaining the detection efficiency of real incidents, is essential in surveillance. For example, alarms are often used to trigger a video recording. In case a forensic search would be needed in alarm-triggered recordings, it could be very time consuming to go through the recorded material if there were many false alarms. Motion detection systems often use video analytics applications that are triggered by a certain amount of pixel changes in the surveillance scene. Unnecessary or ‘false,’ alarms can typically be caused by effects such as moving shadows or light beams, small animals in the scene, rain drops or insects on the camera lens, movements caused by the wind, or bad weather. A detection system based on radar will only detect physical movement in a scene, ignoring purely visual effects such as shadows or light beams. Radar signals should also be generally less affected by rain or snow. In both radar detection and video analytics, it is possible to design the system so that small or swaying objects can be filtered out, as well as certain zones of irrelevant movements caused by, for example, wind in a tree. Complement to cameras A motion detector based on radar, exclusively, will not provide any visual confirmation. To efficiently identify the cause of an alarm, or to enable identification of individuals, the scene should also be monitored by a video camera. To add further value, rules could be established that state that only when both the video camera and the radar detector detect motion in an area will a motion detection alarm be transmitted to the operator or central monitoring station, along with detailed information about the object in motion. Such a collaborative validation can reduce false alarms even further. Axis network radar detector AXIS D2050-VE network radar detector is Axis’ first available radar-based motion detector. It can serve as an affordable complement to security cameras in medium-risk installations, improving detection in challenging conditions and minimizing false alarms. Owing to its advanced tracking algorithm and the positioning information it provides, the detector can also add new features and value to a surveillance system. Detection range and installation One radar detector unit provides accurate detection within a range up to 50m (164ft), within an angle of approximately 120 degrees. For coverage of a larger area, it is possible to use multiple detectors. Typical mounting height should be 3-4m (9-13ft). AXIS D2050-VE can be used as a stand-alone product, but may serve its purpose best as a complement to a camera that also provides a visual view of the scene. In order to facilitate a visual interpretation of the scene, the radar image as it is seen in the user interface can be easily integrated and calibrated with an uploaded reference map. The detector can be treated like a camera in the security system. It is compatible with major video management systems (VMS) and common video hosting systems. The detector comes with Axis open VAPIX interface enabling integration on different platforms. Typical installation scenes include fenced-off areas such as industrial properties or roofs, or parking lots where no activity is expected after hours. However, the detector’s advanced filtering and tracking function makes it valuable in most environments. Figure 2 shows a parking lot as monitored by the network radar detector and shown in the user interface. The radar image has been combined with a reference map of the scene. Include/ exclude zones The network radar detector comes with an intuitive user interface where the user should draw one or more ‘include zones,’ and possibly ‘exclude zones,’ within the detection range. Detection and tracking of objects takes place continuously within the whole detection range. However, owing to its filtering functionality, the detector will trigger actions only on objects detected within an include zone. The filter can also be set to ignore certain object types, and only trigger on, for example, large objects, only vehicles, or objects that have been tracked for a…

Genetec Stratocast™ is a cloud-based video monitoring system that makes the adoption of network video security solutions easy and allows you to connect to your business wherever you go. Using the Microsoft Windows Azure cloud-computing platform, Stratocast eliminates the need for on-site servers. As a result, installation time is reduced and you can begin monitoring your premises quickly. Using video surveillance equipment such as IP (Internet Protocol) cameras or analog cameras, you can record video on your edge recording video unit or in the Stratocast cloud. If recording on your video unit, the video is recorded continuously, whereas if recording in the Stratocast cloud, you can choose to record either continuously or only when motion is detected. From your laptop, tablet, or smartphone, you can then watch live and recorded video that is safely stored in the cloud. In addition, through Genetec Federation™, Security Center users can view and control all Stratocast cameras from their local installation of security desk. The starting diagram illustrates how Stratocast works to keep you connected to your business, wherever you go. Security is crucial for us at every level of development and operations. Based on industry best practices, our engineers embed security standards into the development lifecycle and operations. This white paper focuses on the cloud architecture and the operational security of the platform as well as the security capabilities of the customer portal. The video and camera security of Stratocast are also discussed. Cloud architecture Stratocast is deployed on the Microsoft Azure cloud platform. This platform, with its industry-recognized security, securely stores data that our customers entrust us with. Microsoft Azure has been audited against SOC 1, SOC 2, and SOC 3 standards. Audits are conducted in accordance with SSAE 16 and ISAE 3402 standards. Certifications are regularly updated and can be provided. Stratocast and Azure are also compliant with ISO 27001:2013. The service architecture is built for high availability and scalability, allowing customers to enroll and record as many cameras as needed without impacting the service. There are no constraints limiting the maximum amount of data that can be stored in Azure, as data centers are provisioned with enough capacity to ensure that they meet growing demand. This architecture, coupled with the robustness of the underlying Microsoft Azure Cloud, allows Genetec to provide a 99.5% SLA. Security controls Stratocast and Azure adhere to a rigorous set of security controls that govern operations and support. Genetec and Microsoft deploy a combination of preventive, defensive, and reactive controls including the following mechanisms to help protect against an unauthorized developer and/or administrative activity: Tight access controls, including a mandatory two-factor authentication. Combinations of controls that enhance independent detection of malicious activity. Multiple levels of monitoring, logging, and reporting. Security reports are used to monitor access patterns and to proactively identify and mitigate potential threats. Microsoft administrative operations, including system access, are logged to provide an audit trail if unauthorized or accidental changes are made. Automatic patching of the operating systems and applications running in the cloud. Additionally, the Genetec and Microsoft teams conduct background verification checks of certain operations personnel and limit access to applications, systems, and network infrastructure based on the level of background verification. High availability Azure facilities are designed to run 24x7x365 and employ various measures to help protect operations from power failure, physical intrusion, and network outages. These datacenters comply with industry standards for physical security and availability. They are managed, monitored, and administered by Microsoft operations personnel. Redundancy Stratocast video is stored in triplicate, within the same datacenter, ensuring the redundancy of critical data and mitigating the impact of hardware failure. Control of data location Knowing and controlling the location of an organization’s data can be an important element of data privacy, compliance and governance. Customers can specify the geographic area where their recordings are stored. Through this approach, recordings are replicated within a defined region for redundancy but are not transmitted outside the customer’s desired geographic boundaries. Operational security As a trusted provider of security solutions for a considerable number of government agencies and high-profile public and private organizations worldwide, we take compliance with local regulations very seriously. This, of course, includes the laws pertaining to data security and protection of privacy in the regions where we sell our products and services. Additionally, to ensure that all customer data is stored and used in an appropriate and secure manner, Stratocast is certified with the ISO 27001:2013 information security standard. The ISO 27001 standard is a framework of policies and procedures including legal, physical, and technical controls that address cyber security risks. These policies and procedures are part of the Information Security Management System (ISMS) at Genetec, that has been audited and certified by the ISO organization. Below is an excerpt of some of the relevant portions of it. Secure development policy Genetec is conscious that security is something that has to be embedded in the development practices and not something that can be added after the fact. Consequently, the Stratocast software development lifecycle (SDL) includes specific activities, pertaining to cyber security, that have to be completed in order to release each new version of Stratocast. These activities are defined in the secure development policy and include – secure design review performed on a periodic basis, manual or automated security testing, and penetration testing performed by a 3rd party auditor. Incident management & disaster recovery I t can be challenging to react appropriately to a cyber security incident when it happens, if nothing has been prepared for it beforehand. To avoid this, we have instilled a well-established incident management plan describing appropriate responses. This includes among others – the criteria defining the severity of an incident, the roles and responsibilities of each stakeholder involved in the management of that incident, the incident lifecycle, and the service level objectives. In a similar fashion, it’s best practice to establish a disaster recovery plan in the event of external service outages. Stratocast has a well thought out plan that reduces any negative…

Given the current Covid-19 pandemic, thermal cameras are receiving increasingly more interest. It is a natural idea to utilize a thermal camera to detect elevated body temperatures. Right now, the Internet is full of information on the subject (including brand new companies), but it’s difficult to understand what is real, what is wishful thinking and what is exaggeration. At Eagle Eye Networks we have purchased a number of thermal cameras and have run a series of tests to determine what is practical with today’s technology. This document details some of the testing we have done and some of our conclusions. We do not claim that our testing is comprehensive or perfect, but we hope, that in sharing it, we can help. This document focuses on the application of thermal cameras to read human body temperature. However, it is important to note, that before the Covid-19 pandemic, thermal cameras, at least as it relates to video surveillance, were primarily used for detecting perimeter breaches. This use case does not require the same level of precision that a thermal camera detecting an elevated body temperature requires. Therefore, typical general-purpose thermal cameras in the market have an accuracy of +/- 5 degrees Fahrenheit, which is not accurate enough to detect elevated body temperatures. It’s also important to note that elevated temperature screening is not screening for coronavirus or for any other illness. In fact, some people who have a virus or illness may not have an elevated body temperature. Additionally, the majority of thermal cameras are not approved for medical use or approved by the FDA, but they may be well suited to provide an initial reading to allow appropriate personnel to perform follow up evaluation and potential diagnosis. Executive summary Thermal cameras can be used to detect elevated temperatures in humans under the right conditions. Creating those conditions can be challenging, but it’s not impossible or impractical. Our experience in testing has shown that the preferred solution includes cooperative subjects and limits measurement to a small number of people simultaneously. Given appropriate conditions we have tested cameras and found they consistently report temperatures within +/- 0.7 degrees Fahrenheit of measurements taken with a traditional thermometer. System components There are various systems in the market place; however, most cameras that are connected to a traditional surveillance system include these: Camera – Thermal and Visible Spectrum. Thermal Calibration Unit (blackbody). Recording System/ Video Management System. Local Display Device (optional). Cameras Some of the more advanced thermal cameras are effectively two cameras in a single housing, these are known by several different names – dual spectrum and bi-spectrum are the most common names. The image below (Figure 1) is a dual spectrum camera from Sunell that was designed to resemble a panda bear. This was originally deployed in Chinese schools where children would look at it as they entered. Each camera produces a video stream, the visible spectrum camera works like most typical surveillance cameras. The thermal camera produces an image that is a visual representation of the different temperatures it has detected. These images can be either in grayscale or in color. Most cameras have several visual choices for how to represent the thermal data. The images above (Figure 2) are from a dual spectrum thermal camera connected to the Eagle Eye Cloud VMS. This is a traditional video surveillance dual spectrum thermal camera, not a camera used to detect elevated body temperatures. There are a few things to note about the images. One is that the field of view is different. The visible camera can capture a wider field of view than the thermal camera. The visible camera has two vehicles in the field of view while the thermal camera only has one. The visible camera captures the street at the top of the image, while the thermal camera does not. The difference in camera field of view is quite common. Also, the thermal camera has a much lower resolution. The figures appear more ‘blocky.’ Thermal cameras today are generally much lower resolution than visible spectrum cameras.   Thermal calibration unit A thermal calibration unit, sometimes referred to as a blackbody, is a device that maintains a specific temperature and does not reflect any energy from the surroundings. It is used as a constant point of reference for the thermal camera. Not all thermal cameras require a calibration unit, but many can make use of them if they are present. A calibration unit requires electrical power, but is not wired to the camera or the VMS/ recorder. It is manually set at a prescribed temperature, and the thermal cameras are configured based on that temperature. Thermal calibration units are typically used when more precise temperature readings are required such as in elevated temperature screening. Some suppliers include a thermal calibration unit with the sale of the camera, but most do not. Calibration units are generally not present for most cameras connected to a video surveillance system. Many security industry personnel are not familiar with thermal calibration units or their use. Recording system/ video management system The cameras are generally connected to a recorder. For this discussion we utilized the Eagle Eye Cloud VMS with its enhancements for support of elevated temperature screening. The cameras are connected to an Eagle Eye Bridge. As shown above (figure 2), The Eagle Eye VMS records both the visible spectrum camera as well as the thermal camera. Additionally, Eagle Eye VMS captures the temperature measurement data that the camera generates. This means that the temperature is associated with specific to me, so searches can be performed based on the temperature, time or person. Notifications can be generated if the temperature is outside of a specified range. In other words, if the temperature is too high, a notification can be made. The notifications can be delivered via various methods, but the most common is via email. Typical notifications will have an image of the person, the temperature detected, as well as name and location of the camera that…

  INTRODUCTION Scope and purpose of this white paper The scope of this white paper is the security for embedded electronic systems and IoT systems, which are generally based on programmable microcontrollers. Examples are electronic consumer and industrial devices, IoT sensors, medical devices. The purpose is to stress the fact that although security countermeasures are necessary to protect embedded systems and IoT systems, they are unfortunately not sufficient to avoid surface attacks. Embedded systems and IoT systems are more and more exposed to a wider range of new security threats, and this trend will very probably accelerate. To prevent damages from security attacks, companies are taking measures to protect their assets, including more specifically their software IP. Unfortunately, in ecosystems where the supply chain is getting more complex, it is frequent that the ones deciding the security levels are not the ones that will be accountable for their choices. Even when security measures have been duly selected and implemented, facts are showing that there are still some underlying vulnerabilities. On average, security experts will break security of more than 80% of implementations during their evaluation phase, for multiple reasons: Security attacks are getting easier to set-up, even by players who have limited technical skills and could use tools available on the web. It costs just a few dollars to launch massive DDoS attacks capable of generating up to 300Gb/s. Security countermeasures have their own limitations, and having an overreliance on those countermeasures could lead to potential hidden security risks. Security implementation matters. Technical challenges in implementing security could potentially lead to vulnerabilities exploited by hackers. A good approach is to do a formal security evaluation with security experts. However, before taking this path, it will be efficient and cost effective to have a second view with a deeper dive into security. In most cases, it will highlight some vulnerabilities and will provide useful guidelines to improve the resistance of embedded systems against security attacks. In this whitepaper, we will: Describe the most frequently used security countermeasures. Review the limitations of these countermeasures and explain why a deeper dive is recommended. Share the views from our security experts. The benefit of this deeper dive is to reduce exposure to security attacks without having to reconsider the whole security approach. Security principles Basic principles It is widely accepted that security must rely on 3 basic principles: Security by design (and not after the facts). End to end security (at OT and IT levels). Security all along the product life. The last one is equally important compared to the first two. We observe that several electronic industries are getting conscious about the security by design and end to end security, and are not considering the importance of security all along the product life. For instance, having a secure mechanism for firmware update over the air (OTA) will prevent a lot of security breaches. Deeper dive I t would be great if a simple application of those basic principles will be enough to counter any potential security attack. Facts are showing that even by applying those principles, there are still remaining vulnerabilities exploited by hackers. Embedded systems are all different and have their own specificities; on the other side, security requirements vary considerably depending on market, applications or risk management policies. Considering that security must be scalable, and that no security scheme fits all, we recommend a deeper dive into security to ensure that the security schemes have been implemented in adequation with the system architecture. A strong security scheme which has not been properly implemented is simply useless. We will explain in this white paper the reasons why these basic security principles are necessary and not sufficient. Disclaimer The information in this white paper provides general information and guidance about cybersecurity; it is not intended as legal advice nor should you consider it as such. WHY DOES A DEEPER DIVE INTO SECURITY MAKE SENSE? Security attacks on embedded systems are getting more frequent There are several reasons that could explain why embedded and IoT systems are getting more vulnerable to security attacks: Systems complexity Embedded and IoT systems are becoming more and more complex due to rich, broad and diverse ecosystems which could be interconnected with each other’s. IoT ecosystems are an illustration of this trend; they include a wider range of technologies like sensors, gateways, networks, clouds with many different standards and limited regulations on security. Limited capacities in devices Many embedded and IoT systems are based on programmable microcontrollers with limitations in processing power and memory storage. Several security countermeasures have not been designed based on those limitations. As a result, they require compromising between security and performance, and most of the time the decision is in favor of the last one. Human errors are always possible The development of new technologies is accelerating, and we do not have enough background of previous threats to know enough about failures in protection. This is leading to an increase of human errors in life of a product – at the design stage, at manufacturing stage and during the implementation of security. Time to market and costs Generally, manufacturers shorten the launch time of products, putting higher priorities on volume of sales, and not always considering fundamental security best practices such as security by design. Security is often seen as an additional cost; this is why, in order to reduce costs, manufacturing companies are also limiting or ignoring security features in their devices. The result would be equipment that can never provide adequate protection. Any countermeasure has its own limitations Deciding a security strategy often means making compromises between risk, cost and time – the easier approach is to rely on legacy security mechanisms proposed by silicon and IP vendors, network providers or other third parties in the value chain. The issue with this approach is that there is no ‘one size fits all’ security solution that can protect any embedded system. The characteristics of each system is different and should be considered…

With the increased prevalence of  IP-based video surveillance systems on the market, and the growing adoption of higher resolution HD and megapixel cameras, organizations and system integrators must take into account how implementing these systems can impact their network resources. Without realistic system design considerations, organizations can risk significant network and storage cost overruns while also compromising the reliability of their network to support applications that are critical to their business operations. By implementing an advanced video management system (VMS), an organization can effectively manage video streams on their network using built-in camera and software functionalities to optimize network resources and bandwidth consumption. With such optimizations, a VMS will also help to decrease networking and storage costs over the lifetime of a video surveillance system. Challenges For organizations choosing to implement or expand an IP video surveillance system, the ability to efficiently manage video streams and storage is crucial to ensure the best use of the network and reduce costs associated with deploying and operating the system. While organizations continue to benefit from greater network speeds and capacity, the use of IP-based video systems can generate a significant increase in the amount of data traveling on their network as a result of: Deploying high-definition and megapixel cameras, Additional cameras to address a need for coverage across Larger areas, Increases to the number of users accessing video, Recording and maintaining redundant video recordings, Transferring video from one site to another to maintain long-term orcentralized recordings. When planning and designing an IP video surveillance system, an organization must take into account the unique aspects of its security environment and its business operations in order to ensure the reliable transmission of video and avoid overloading available network resources. For example, certain deployments will require greater flexibility to manage video streams and bandwidth due to their complex nature,further driving the need for advanced video management capabilities. These scenarios can include: Distributed sites requiring operators to connect to remote cameras, Cameras connected to networks with limited bandwidth such as DSL, wireless, or cellular, Sharing bandwidth with other operation-critical applications because video is not the top priority for the business. It is equally important for organizations to realize that optimizing the use of network resources does not necessarily require large capital investments but is more a matter of putting the right solutions in place. With bandwidth and storage representing important ongoing costs of operating an IP system, organization scan significantly reduce the Total Cost of Ownership (TCO) of their video surveillance system by investing in solutions that allow them to optimize their use of bandwidth and storage based on the requirements of their application. This white paper will focus on those unique and powerful capabilities that one should look for in a VMS in order to optimize the use of network resources and reduce the costs associated with operating an IP-based surveillance system. Optimizing network resource utilization VMS applications allow an organization to manage its security infrastructure including video cameras, encoders, and recording servers, within the unique context of the organization’s deployment. The effectiveness of the VMS will depend on its ability to handle the demands of the operating environment, whether those demands include deploying a system in sites with limited bandwidth, monitoring cameras across distributed locations, or ensuring that multiple operators can access necessary video streams in the case of an incident, regardless of the number of concurrent requests. Although system administrators will intuitively manage video quality settings and define recording settings and schedules, addressing the needs of a specific security department can require manual intervention and adjustment. While most VMS applications support these features, some VMS applications also support powerful functionalities and technologies that serve to further reduce the total cost of operating an IP video system. In fact, organizations can deploy a surveillance system that operates with greater efficiency on their network by choosing a VMS application that supports the following capabilities: End-to-end multicast transmission, Stream redirection and multicast-to-unicast conversion, Multi-streaming, Video caching, Archive transfer. By leveraging some or all of these capabilities, organizations can significantly reduce the number of servers required to manage and store video, reduce their network bandwidth requirements, and reliably scale their system while minimizing their investment in new infrastructure. A. Video stream transmission: unicast and multicast I n IP video surveillance, unicast and multicast are the two most commonly used methods to transmit video from cameras to client workstations. While all VMS platforms can configure unicast, only a few also offer multicast transmission, and, among these, even fewer support end-to-end multicast that provides communication from the edge device (IP cameras and encoders) to the workstation. Though many VMS platforms may claim multicast support, the majority will only provide limited support for multicast transmission between the recording server and the client station, and require multicast to be set for all cameras on the server, or even implemented system wide. It is important for organizations to consider that certain VMS provide far greater flexibility with regards to transmission, in order to implement the best design for their application. This includes the ability to set up cameras per select network branch or per viewer and the ability to automatically detect the ideal transmission method for different segments of the network, thereby allowing organizations to optimize the performance of their video surveillance system and minimize the network resources that are required. i. Unicast overview Unicast is usually done in TCP or UDP and requires a direct connection between the source and the destination. Unicast only works if the source has the capability to accept concurrent connections when multiple destinations want to view or record the same video at the same time. In IP video surveillance, unicast involves a camera streaming as many copies of the video feed as are requested by the destinations, so a 6 Mbps video stream that is requested by three operators will produce a transmission of 18 Mbps of data across multiple network segments (6 Mbps per stream x 3 requests = a total of 18 Mbps). This…

Around the world, governments are responding to the unprecedented circumstances related to the coronavirus (COVID-19) epidemic. In many countries and regions, authorities have placed restrictions on their citizens movements and have increased guidance on the basic hygiene required to reduce the spread of the virus. The primary aim of this activity is to reduce the reproduction number (Ro ) of COVID-19 by limiting contact between groups of people as much as possible. Similarly, many government and healthcare authorities have provided guidance on the key symptoms associated with the disease. One of the key symptoms is an increased body temperature or fever. How can thermal cameras help? There are several activities and approaches being applied to help reduce the reproduction rate of COVID-19. These include self-isolation methods such as working from home, improved basic hygiene such as increased hand washing and the deployment of personal protective equipment (PPE) to reduce the prospect of infection. Similarly, when symptoms appear there is clear guidance on what to do next. Primarily this involves limiting social contact through self-isolation for up to 14 days. Medical professionals should be contacted digitally if symptoms persist or deteriorate. Ultimately, prior to any vaccine being available, the fight against COVID-19 is being led by the ability to detect symptoms and isolate people suspected of an infection. This is a combined effort between different key workers and technology applications. Thermal cameras can play a part in this coordinated approach. These cameras provide thermal imaging for body temperature solutions which can quickly and accurately identify people with elevated body temperatures, one of the key symptoms of COVID-19. These solutions can provide organizations with an additional layer of protection to their facility from increased exposure to the coronavirus. Organizations can then decide how best to deploy this information based on region, culture and the critical nature of the facility. In some circumstances a security officer may ask the person to scan their temperature using a medically approved sensor. In others, the person may be denied access to the facility. Ultimately, it is a decision for each organization on how best to deploy the solution. Thermal body temperature solutions An important distinction to make in the overall societal response to COVID-19 is that body temperature solutions are not a medical solution. They cannot identify the virus and they do not protect organizations or individuals from catching the virus. Thermal body temperature solutions are a tool that can support the identification of a key symptom of the disease. They can help organizations identify people showing these symptoms, but they do not diagnose or treat COVID-19. However, this does not mean that thermal body temperature solutions do not add value in the overall response. In fact, they provide a non-invasive method to check body temperature, can do this at faster rates than hand-held scanners and at a greater (potentially safer) distance. The deployment of these solutions in a facility may even encourage positive behaviour with staff more likely to stay at home when they are unwell with a fever. Thermal body temperature solutions require, at a minimum, a radiometric thermal camera to measure temperature differences in people entering the field of view. More advanced solutions will use blackbody devices to help calibrate the temperature measurement, especially in less controlled environments where the elements can influence the reading. AI (artificial intelligence) algorithms can also be integrated to help target the temperature reading on the most accurate part of the body, typically the forehead or near the eyes. The blackbody calibration tool consists of a target object whose temperature is precisely known and controlled. Specifically, this is important in human temperature measurement where accuracy to +/- 0.3 degrees Celsius is advised by many international standards organizations. By deploying the blackbody calibration tool, it is easier to establish an accurate relationship between gray level and temperature. Essentially there is known, fixed temperature object in the field of view which can be used to calibrate and measure all other objects’ temperatures. Using this method, false temperature alarms caused by environmental influence can be effectively reduced, and the accuracy of the thermal body temperature solution can be controlled to more precise parameters. However, monitoring accuracy does depend on the stability of the body temperature and it is recommended to install the system in a stable environmental condition to ensure that the skin temperature is stable. The emergence of AI technology, and specifically face detection algorithms, will play an important role in the evolution of these solutions too. Algorithms can help complete more accurate temperature tests. Cameras can do this by locating specific areas of the face such as the forehead or eyes, more accurately. This could be critical in the case of people wearing masks. Combining thermal cameras and facial detection can enable thermal body temperature solutions to combine accurate temperature scanning with the best face location to take the measurement from, improving the overall measurement accuracy. It should also be noted that the facial detection, as opposed to recognition, is used to improve the accuracy of the solution with better positioning of the measuring point on the face. It is not used to detect specific individuals and does not break privacy compliances (such as GDPR). While there remain challenges to the effectiveness of thermal imaging cameras for measuring human body temperature in public areas, especially when face masks are commonplace, the introduction of facial detection and AI can improve the accuracy of temperature scanning. Managing expectations for use Comparisons can be made between the current stage of the market for thermal body temperature solutions and another physical security technology – video analytics. Here, the expectation level for object detection or activity tracking algorithms was extremely high. The expectation was that video analytics would be near 100 percent accurate in spotting, identifying and tracking objects through the field of vision. However, analytics would sometimes misunderstand a scene, potentially alerting to the same object multiple times or mis-allocating an object – essentially false alerts. The reality was that these solutions…

Businesses are using sensors, Internet of Things (IoT) devices, and surveillance cameras to manage assets and resources more efficiently than ever before. Facial recognition, remote patient monitoring, and wrong-way driver detection are just a few of the advanced, insight-driven technologies seeing greater adoption today. At the center of it all is data, which is continuously being gathered, analyzed and utilized for real-time decision-making. This data collection places a greater workload on the storage systems behind the sensors. Smart solutions are only as good as the data they store, analyze and deliver in a timely manner. This white paper discusses rapid changes in the global data-sphere, the impact of real-time data analysis in safe and smart cities, and the storage best practices that system integrators should implement to improve data flow and insights for customers. Global Data-sphere Evolution Data is in flight all around us and has become an essential part of the human experience. The global market intelligence firm – IDC – forecasts that the global data-sphere will increase from 33 zettabytes in 2018 – where one zettabyte equals to a trillion gigabytes – to 175 zettabytes in 2025. That is by 2025, on an average every connected person in the world will have a digital data engagement over 4900 times per day. This breaks down to about 1 digital engagement every 18 seconds. IDC reports that the number of IoT devices will grow to 80 billion by 2025, and these smart solutions will monitor business processes and enhance everyday life activities. Harnessing the Power of Data Executives are ultimately looking to interpret the data aggregated by IoT devices, sensors and security solutions, and leverage it to improve operations, cost-savings and customer satisfaction. The deployments of cognitive systems such as machine learning, natural language processing and AI that actively analyze this data for proactive decision-making are on the rise. IDC indicates that the amount of analyzed data that is ‘touched’ by cognitive systems will grow by a factor of 100 to 1.4 zettabytes in 2025. The use of cognitive systems is opening the door to new business opportunities and a greater return on investment in all markets. Storage in the Era of AI New enhancements allowing security solutions to be used for business intelligence are driving the demand for data-hungry applications. The increased use of AI systems in security has warranted a shift in recording and storage technologies. Standard surveillance systems primarily recording footage were typically write-only applications. Today surveillance systems with AI have mixed read/ write workloads. Previously, users relied on cloud data centers to manage the unstructured data and analysis. However, this setup often causes latency and delays as all video and metadata must be transferred off-site for analysis. To remedy this issue, storage providers are building AI into video NVR systems and harnessing the power of micro-datacenters so that initial processing, analysis and pattern recognition may occur in real time at the edge. The edge refers to servers and appliances outside of data centers that are located regionally and are closer to endpoints, like surveillance cameras and sensors, where the data is first captured. Development of AI-enabled NVRs and edge computing devices is driven by cheaper graphics processing units (GPU) with enhanced analysis capabilities, as well as better storage options. In particular, new hard disk drives with fast writing data speeds, high read performance, and support for both AI and video workloads have become attractive solutions for system integrators. Innovation in telecommunications with 5G, advanced sensors and intelligent surveillance cameras are also driving the evolution of surveillance beyond traditional security for AI applications. After the initial video ingestion and analytics at the edge, video is pushed to the back end or cloud. In this centralized environment, video and AI metadata are consolidated for deep learning activities to train the system to be more predictive and provide a more holistic view of the video data collected. In the past, users primarily used cloud storage to satisfy legal and corporate retention policies; however, that has since changed. Data no longer languishes in the back end to eventually be discarded. Now data in the cloud is used to bring predictive power and intelligence for better decision-making like never before. Ultimately, implementing robust storage solutions from edge to cloud enables smarter surveillance systems over time through AI training and rapid insights for command center operators to quickly respond to time-sensitive scenarios. Biggest Impact: Safe and Smart Cities The development of safe and smart cities continues to be one of the sectors where surveillance systems and data will have the greatest impact. Research firm IHS Markit indicates that the global market for city surveillance exceeded $3 billion in 2017 and is expected to increase each year by 14.6% from 2016 through 2021. IHS reports that China is one of the strongest adopters of safe city surveillance technologies. SMART CITY SECTORS Beyond citywide surveillance, smart cameras, IoT sensors, and edge computing devices with AI are being deployed in smart cities to equip businesses and citizens with data that can enhance the urban experience. IHS Markit predicts that there will be at least 88 smart cities worldwide, a substantial increase from 21 cities in 2013. The collection and delivery of data is the crux of the smart cities operation. Here are three segments within smart cities where organizations are using data to address urban woes. HEALTHY LIVING AND SAFETY Data captured by IoT devices and video are utilized to not only improve the quality of healthcare, but also the kind of preventative measures implemented to ensure a healthier population. In hospitals, IoT sensors and video devices enable re mote patient monitoring, providing real-time alerts of blood pressure and other body indicators to staff who can intervene before a situation escalates to a crisis. The end result is lower mortality rates. When it comes to preventive health measures, advanced IoT devices and video solutions are employed for air quality monitoring, alerting sensitive groups to potentially dangerous conditions. Population health programs are using data collection…

See no evil, block no evil Imagine if one could see deep into the future, and way back into the past – both at the same time. Imagine having visibility of everything that had ever happened and everything that was ever going to happen, everywhere, all at once. And then imagine processing power strong enough to make sense of all this data in every language and in every dimension. Unless you’ve achieved that digital data nirvana (and you haven’t told the rest of us), you’re going to have some unknowns in your world. In the world of security, unknown threats exist outside the enterprise in the form of malicious actors, state-sponsored attacks and malware that moves fast and destroys everything it touches. The unknown exists inside the enterprise in the form of insider threat from rogue employees or careless contractors – which was deemed by 24% of the survey respondents to pose the most serious risk to their organizations. The unknown exists in the form of new devices, new cloud applications and new data. The unknown is what keeps CISOs up at night. This report sheds light on what actions are reaping results in strengthening organizational cyber health. For example, when asked, only 35% confirm that it is easy to determine the scope of a compromise, contain it and remediate from exploits. It suggests that visibility into the unknown clearly is a key challenge. It means 65% of CISOs in the survey have room to improve. 46% said that they have tools in place that enable them to review and provide feedback regarding the capabilities of their security practices. While the good fight is far from over, it’s also far from being all bad news. At least some respondents in the survey seem to be feeling good about their jobs. When asked about cyber fatigue, only 30% of respondents claimed to suffer from cyber fatigue this year. While almost a third seems like a high number to be tapping the mat and raising the white flag, the drop from last year’s figure of 46% is moving in the right direction and this is worth the fight. State of the CISO For some time now, threat hunters have talked about knowing the unknowns. It’s time to expand that to the entire spectrum of cybersecurity – to users, apps, data and clouds. You can’t protect what you can’t see. You generally want to support the business, and not mire it down in bureaucracy. If you’re going to be a bit more open, how are you mitigating control? This is going to be different for everyone. CISOs must deal with that balance of organizational culture while combatting the most critical threats. Sometimes blocking everything and locking everything down doesn’t fit the culture of the enterprise. That might be right for a bank but not for a university. The CISO faces several challenges managing cyber-risk – whatever their organizational model: Breaches create adverse impacts to financial profitability, brand reputation, customer data security, customer satisfaction, and continuity of business. Losses can be substantial and non-recoverable, creating a higher risk score for the organization on insurability. Over the years, vendor point solutions looked promising; however, each generates their own set of alerts. Many point solutions competing on alerts makes it difficult to identify those threats posing the highest risk to the organization, and becomes a resource drain. IT is usually siloed across the organization, making inte gration of securing the network, the cloud, and employee endpoints highly complex. Aggressive tactics to hire security IT personnel are required, as the specialized pool of candidates cannot sustain the magnitude of the problem across global organizations. The talent shortage is, however, out of control and not solvable by trying to fill all jobs. New threats such as Emotet, Olympic Destroyer and others appear daily, even hourly, and are employing more stealth and sophisticated methods. Threat response as a category has to evolve and there is a need for tools to consolidate information and centralize remediation of infections and other incidents. Additional technologies and processes for the CISO to consider are: AI and ML, and used right are essential to triage the volume of work. The cost of a breach is falling – but don’t get too excited yet. There is head room to realize obvious benefits in process improvement e.g., training. There is more confidence in cloud-delivered security and in securing the cloud. 2019 findings The findings from the Benchmark Study revealed several areas that are critical to strengthening organization’s security posture. Set up for success? What does it mean to be a CISO day-by-day? What is their charter? The present survey revealed multiple areas that together determine a organization’s cyber health including being practical about risk, setting criteria for budgeting, collaborating across divisions, educating staff, conducting drills, knowing how to track outcomes to inform investments, and being strategic on vendor and solution implementation. Know your risk Risk management is hardly table stakes. Understanding the risks of cyberattacks and the compliance landscape that encompasses security breaches is paramount to understanding how to defend and prepare for the worst. When asked who were very knowledgeable about risk and compliance, only 80% of respondents were very knowledgeable. That leaves 20% of security professionals who could possibly use some of the discussed trainings. How to spend budget Almost half, or 47% are determining how to control security spending based on organizational security outcome objectives. Measuring outcomes against investments is the best data-driven approach. What’s more, 98% strongly or somewhat agree that their executive team has established clear metrics for assessing the effectiveness of their security program. 49% of respondents have metrics that are utilized by multiple areas of their companies to understand the risk- based decisions and improve processes to measure the security effectiveness throughout the organization. Back to the budget, and aside from outcome based measurement, there are some less healthy options. Controlling security spending on previous years’ budgets (46%) and percent of revenue respectively (42%) were both popular choices,…

Successfully recording video from hundreds of security cameras 24 hours a day, seven days a week without losing a single frame is a very complex challenge. If that wasn’t tough enough, your system also has to allow for future growth and show that it’s reducing your claims and/ or shortening response times. Video surveillance is becoming more and more important as perceived and actual physical security threats increase worldwide. Hardware and solutions proliferate even as budgets have flattened or turned downward. Whether you’ve been a security professional for decades or your IT department just inherited video surveillance, there’s a morass of technologies to wade through to find the right components. This white paper focuses on how to specify video storage, how video is unique in the world, and why systems must be carefully thought out to ensure crucial data isn’t lost. Central to the discussion is a review of the trio of storage technologies – Direct Attached Storage (DAS), Network Attached Storage (NAS), and Storage Area Network (SAN). Before diving into the details of each of these technologies, let us look at some broader considerations to have in mind while writing video surveillance system requirements for your building, complex or campus. Important considerations for building and upgrading surveillance systems You spend hours Googling ‘surveillance systems’ or an entire week at a trade show and easily come away confounded by the plethora of security hardware, software and services. In this first section, we outline the problem and a few considerations to keep in mind while developing the requirements for your physical security system. The conundrum of surveillance Thirty years ago, you simply went out and bought some cameras, coaxial cable and a VCR. Now, all the components are digital. The surveillance conundrum is clear to anyone who follows the news: Threats (real and perceived) are growing. In response, the public, private companies and govern ments are demanding more and better physical security. Surveillance options are growing in number and capability. The growing camera population (with ever higher resolution) is creating a flood of data. Cameras never stop recording and what they ‘see’ must be stored somehow. Despite event-driven spikes, security budgets have generally declined in recent years. A growing demand for surveillance Beyond public security, there’s an ever-growing demand for video surveillance inside and around banks, casinos, school campuses, hospitals, hotels, transportation hubs and highways, railways, harbors, factories, power plants, refineries etc. Of course, many of these systems can serve a dual purpose such as speeding up ferry departures based on traffic conditions, remotely-monitoring trucks as they’re being loaded, or alerting hotel staff of a VIP’s arrival. “With the edge getting smarter, it is the time that the backend Infrastructure gets smarter too. With ML & AI being deployed extensively, data is the ‘new gold’ and its reliability needs to be addressed as well” – Prakash Prabhu Regional Sales Director – SAARC, Pivot3 Analytics: The shift to digital transforms watchers into actors Sci-fi and action films may offer windows into the future of surveillance, but today you can tap into the wealth of information found in the real world. When tied to biometric reads (such as iris scanners) and using behavioral analysis algorithms, video surveillance systems can now monitor numerous real-time scenes and automatically respond with say – a coupon to a shopper who shows interest in a particular shirt, or an alert to unusual activity in a subway station. With automation, human eyes aren’t needed for the mind-numbing task of watching a bank of video monitors. Personnel can instead focus on stopping a bad guy in the act, correcting a problem, anticipating a need, or providing a service. In fact, advanced users are turning their surveillance data from cost centers into cash. Digital equipment and software The shift to digital has also changed the way video surveillance systems are built. Instead of endless ‘home runs’ of coaxial and power cables from a control room to each camera, IP (Internet Protocol) cameras and monitors can be networked just like computers. Cameras can even be powered by the same Ethernet cabling that transmits their video data. Today’s video surveillance systems typically have at least one computer server running video management software (VMS). The VMS enables users to control the cameras and monitors, as well as search archived ‘footage’ in storage. Storage can either be inside the VMS server (as DAS in a NVR) or in a separate storage device on the network (NAS or SAN). Computer processing and storage infrastructure software underlies the VMS application layer, ensuring that all your equipment is working as it should, with little or no administrative burden. You can also run all the software and storage on virtual machines. Ever-improving cameras mean ever-growing data streams Whether you are securing a small office or large factory campus, now that cameras are digital, you’re able to take advantage of Moore’s Law and watch prices droping as sophistication soars. However, a lower price also suggests the temptation of buying more. Better capabilities (like high-definition) offer better detail in a wider range of light conditions. With a 180° or 360° view, one camera can do the work of several analog eyes. For example, the wide angle can enable you to watch an entire parking lot, then pan or zoom electronically to read a license plate or see a face. The downside is such cameras require a huge amount of network bandwidth and storage. As the name suggests, each IP camera has its own IP address and connects to the network with a standard RJ-45 jack. It often has a built-in web server, email client, FTP client and supports Power-over-Ethernet (PoE) standards. As IP cameras become more sophisticated, they’re able to stream to more than one destinations; perform more processing and analytics; and make adjustments for changing environmental conditions (such as rain or fog) and lighting changes; and reduce frame rates if a scene is unchanged (thereby lowering bandwidth and storage loads). System performance is measured in terms of how many…