Messoa Network Video Recorders (NVR) / Network DVRs

In 2017, IoT-based cyberattacks increased by 600%. As the industry moves towards the mass adoption of interconnected physical security devices, end users have found a plethora of advantages, broadening the scope of traditional video surveillance solutions beyond simple safety measures. Thanks in part to these recent advancements, our physical solutions are at a higher risk than ever before. With today’s ever evolving digital landscape and the increasing complexity of physical and cyber-attacks, it’s imperative to take specific precautions to combat these threats. Video surveillance systems Cybersecurity is not usually the first concern to come to mind When you think of a video surveillance system, cybersecurity is not usually the first concern to come to mind, since digital threats are usually thought of as separate from physical security. Unfortunately, these two are becoming increasingly intertwined as intruders continue to use inventive methods in order to access an organisation's assets. Hacks and data breaches are among the top cyber concerns, but many overlook the fact that weak cybersecurity practices can lead to physical danger as well. Organisations that deploy video surveillance devices paired with advanced analytics programs often leave themselves vulnerable to a breach without even realising it. While they may be intelligent, IoT devices are soft targets that cybercriminals and hackers can easily exploit, crippling a physical security system from the inside out. Physical security manufacturers Whether looking to simply gain access to internal data, or paralyse a system prior to a physical attack, allowing hackers easy access to surveillance systems can only end poorly. In order to stay competitive, manufacturers within the security industry are trading in their traditional analogue technology and moving towards interconnected devices. Due to this, security can no longer be solely focused on the physical elements and end users have taken note. The first step towards more secured solutions starts with physical security manufacturers choosing to make cybersecurity a priority for all products, from endpoint to edge and beyond. Gone are the days of end users underestimating the importance of reliability within their solutions. Manufacturers that choose to invest time and research into the development of cyber-hardening will be ahead of the curve and an asset to all. Wireless communication systems Integrators also become complicit in any issues that may arise in the future Aside from simply making the commitment to improve cyber hygiene, there are solid steps that manufacturers can take. One simple action is incorporating tools and features into devices that allow end users to more easily configure their cyber protection settings. Similarly, working with a third party to perform penetration testing on products can help to ensure the backend security of IoT devices. This gives customers peace of mind and manufacturers a competitive edge. While deficient cybersecurity standards can reflect poorly on manufacturers by installing vulnerable devices on a network, integrators also become complicit in any issues that may arise in the future. Just last year, ADT was forced to settle a $16 million class action lawsuit when the company installed an unencrypted wireless communication system that rendered an organisation open to hacks. Cybersecurity services In addition, we’ve all heard of the bans, taxes and tariffs the U.S. government has recently put on certain manufacturers, depending on their country of origin and cybersecurity practices. Lawsuits aside, employing proper cybersecurity standards can give integrators a competitive advantage. With the proliferation of hacks, malware, and ransomware, integrators that can ease their client's cyber-woes are already a step ahead. By choosing to work with cybersecurity-focused manufacturers who provide clients with vulnerability testing and educate end users on best practices, integrators can not only thrive but find new sources of RMR. Education, collaboration and participation are three pillars when tackling cybersecurity from all angles. For dealers and integrators who have yet to add cybersecurity services to their business portfolios, scouting out a strategic IT partner could be the answer. Unlocking countless opportunities Becoming educated on the topic of cybersecurity and its importance for an organisation is the first step Physical security integrators who feel uncomfortable diving headfirst into the digital realm may find that strategically aligning themselves with an IT or cyber firm will unlock countless opportunities. By opening the door to a partnership with an IT-focused firm, integrators receive the benefit of cybersecurity insight on future projects and a new source of RMR through continued consulting with current customers. In exchange, the IT firm gains a new source of clients in an industry otherwise untapped. This is a win for all those involved. While manufacturers, dealers and integrators play a large part in the cybersecurity of physical systems, end users also play a crucial role. Becoming educated on the topic of cybersecurity and its importance for an organisation is the first step. Commonplace cybersecurity standards Below is a list of commonplace cybersecurity standards that all organisations should work to implement for the protection of their own video surveillance solutions: Always keep camera firmware up to date for the latest cyber protections. Change default passwords, especially those of admins, to keep the system locked to outside users. Create different user groups with separate rights to ensure all users have only the permissions they need. Set an encryption key for surveillance recordings to safeguard footage against intruders and prevent hackers from accessing a system through a backdoor. Enable notifications, whether for error codes or storage failures, to keep up to date with all systems happenings. Create/configure an OpenVPN connection for secured remote access. Check the web server log on a regular basis to see who is accessing the system. Ensure that web crawling is forbidden to prevent images or data found on your device from being made searchable. Avoid exposing devices to the internet unless strictly necessary to reduce the risk of attacks.

When a child goes missing in a large, crowded mall, we have a panicking mom asking for help from the staff, at least a dozen cameras in the area, and assuming the child has gone missing for only 15 minutes, about 3 hours’ worth of video to look through to find the child. Typical security staff response would be to monitor the video wall while reviewing the footage and making a verbal announcement throughout the mall so the staff can keep an eye out for her. There is no telling how long it will take, while every second feels like hours under pressure. As more time passes, the possible areas where the child can be will widen, it becomes more time-consuming to search manually, and the likelihood of finding the child decreases. What if we can avoid all of that and directly search for that particular girl in less than 1 second? Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streamsWith Artificial Intelligence, we can. Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streams in a fraction of a second, using only one photo of that person. The photo does not even have to be a full frontal, passport-type mugshot; it can be a selfie image of the person at a party, as long as the face is there, the AI can find her and match her face with the hundreds or thousands of faces in the locations of interest. The search result is obtained in nearly real time as she passes by a certain camera. Distinguishing humans from animals and statues The AI system continuously analyses video streams from the surveillance cameras in its network, distinguishes human faces from non-human objects such as statues and animals, and much like a human brain, stores information about those faces in its memory, a mental image of the facial features so to speak. When we, the system user, upload an image of the person of interest to the AI system, the AI detects the face(s) in that image along with their particular features, search its memory for similar faces, and shows us where and when the person has appeared. We are in control of selecting the time period (up to days) and place (cameras) to search, and we can adjust the similarity level, i.e., how much a face matches the uploaded photo, to expand or fine-tune the search result according to our need. Furthermore, because the camera names and time stamps are available, the system can be linked with maps to track and predict the path of the person of interest. AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight Protecting people’s privacy with AI Face Search  All features of face recognition can be enabled by the system user, such as to notify staff members when a person of interest is approaching the store AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight. First, with AI Face Search, no names, ID, personal information, or lists of any type are required to be saved in the system. The uploaded image can be erased from the system after use, there is no face database, and all faces in the camera live view can be blurred out post-processing to guarantee GDPR compliance. Second, the lack of a required face database, a live view with frames drawn around the detected faces and constant face matching in the background also significantly reduces the amount of computing resource to process the video stream, hence the lightweight. Face Search versus Face Recognition AI Face Search Face Recognition Quick search for a particular person in video footage Identify everyone in video footage Match detected face(s) in video stream to target face(s) in an uploaded image Match detected face(s) in video stream to a database Do not store faces and names in a database Must have a database with ID info Automatically protect privacy for GDPR compliance in public places May require additional paperwork to comply with privacy regulations Lightweight solution Complex solution for large-scale deployment Main use: locate persons of interest in a large area Main use: identify a person who passes through a checkpoint Of course, all features of face recognition can be enabled by the system user if necessary, such as to notify staff members when a person of interest is approaching the store, but the flexibility to not have such features and to use the search tool as a simple Google-like device particularly for people and images is the advantage of AI Face Search.Because Face Search is not based on face recognition, no faces and name identifications are stored Advantages of AI Face Search Artificial Intelligence has advanced so far in the past few years that its facial understanding capability is equivalent to that of a human. The AI will recognise the person of interest whether he has glasses, wears a hat, is drinking water, or is at an angle away from the camera. In summary, the advantages of Face Search: High efficiency: a target person can be located within a few seconds, which enables fast response time. High performance: high accuracy in a large database and stable performance, much like Google search for text-based queries. Easy setup and usage: AI appliance with the built-in face search engine can be customised to integrate to any existing NVR/VMS/camera system or as a standalone unit depending on the customer’s needs. The simple-to-use interface requires minimal training and no special programming skills. High-cost saving: the time saving and ease of use translate to orders of magnitude less manual effort than traditionally required, which means money saving. Scalability: AI can scale much faster and at a wider scope than human effort. AI performance simply relies on computing resource, and each Face Search appliance typically comes with the optimal hardware for any system size depending on the customer need, which can go up to thousands of cameras. Privacy: AI Face Search is not face recognition. For face recognition, there are privacy laws that limits the usage. Because Face Search is not based on face recognition, no faces and name identifications are stored, so Face Search can be used in many public environments to identify faces against past and real-time video recordings. AI Face Search match detected face(s) in video stream to target face(s) in an uploaded image Common use cases of AI Face Search In addition to the scenario of missing child in a shopping mall, other common use cases for the AI Face Search technology include: Retail management: Search, detect and locate VIP guests in hotels, shopping centres, resorts, etc. to promptly attend to their needs, track their behaviour pattern, and predict locations that they tend to visit. Crime suspect: Quickly search for and prove/disprove the presence of suspects (thief, robber, terrorist, etc.) in an incident at certain locations and time. School campus protection: With the recent increase in number of mass shootings in school campuses, there is a need to identify, locate and stop a weapon carrier on campus as soon as possible before he can start shooting. Face Search will enable the authorities to locate the suspect and trace his movements within seconds using multiple camera feeds from different areas on campus. Only one clear image of the suspect’s face is sufficient. In the race of technology development in response to business needs and security concerns, AI Face Search is a simple, lightweight solution for airports, shopping centres, schools, resorts, etc. to increase our efficiency, minimise manual effort in searching for people when incidents occur on site, and actively prevent potential incidents from occurring. By Paul Sun, CEO of IronYun, and Mai Truong, Marketing Manager of IronYun

With increased demands being placed on safety and security globally, and supported by advancements in IP cameras and 360-degree camera technology, the video surveillance industry is growing steadily. Market research indicates that this worldwide industry is expected to reach an estimated $39.3 billion in revenue by 2023, driven by a CAGR of 9.3 percent from 2018 to 2023. Video surveillance is not just about capturing footage (to review an event or incident when it occurs), but also about data analysis delivering actionable insights that can improve operational efficiencies, better understand customer buying behaviours, or simply just provide added value and intelligence. Growth of Ultra-HD surveillance To ensure that the quality of the data is good enough to extract the details required to drive these insights, surveillance cameras are technologically evolving as well, not only with expanded capabilities surrounding optical zoom and motion range,4K Ultra HD-compliant networked cameras are expected to grow from 0.4 percent shipped in 2017, to 28 percent in 2021 but also relating to improvements in signal-to-noise (S2N) ratios, light sensitivities (and the minimum illumination needed to produce usable images), wide dynamic ranges (WDR) for varying foreground and background illumination requirements, and of course, higher quality resolutions. As such, 4K Ultra HD-compliant networked cameras are expected to grow from 0.4 percent shipped in 2017, to 28 percent in 2021, representing an astonishing 170 percent growth per year, and will require three to six times the storage space of 1080p video dependent on the compression technology used. Surveillance cameras are typically connected to a networked video recorder (NVR) that acts as a gateway or local server, collecting data from the cameras and running video management software (VMS), as well as analytics. Capturing this data is dependent on the communications path between individual cameras and the NVR. If this connection is lost, whether intentional, unintentional, or a simple malfunction, surveillance video will no longer be captured and the system will cease operations. Therefore, it has become common to use microSD cards in surveillance cameras as a failsafe mechanism. Despite lost connectivity to the NVR, the camera can still record and capture raw footage locally until the network is restored, which in itself, could take a long time depending on maintenance staff or equipment availability, weather conditions, or other unplanned issues. Since microSD cards play a critical role as a failsafe mechanism to ensure service availability, it is important to choose the right card for capturing video footage.  It has become common to use microSD cards in surveillance cameras as a failsafe mechanism if an NVR breaks Key characteristics of microSDs There are many different microSD cards to choose from for video capture at the network’s edge, and they range from industrial grade capabilities to commercial or retail grade, and everything in-between. To help make some of these uncertainties a little more certain, here are the key microSD card characteristics for video camera capture. Designed for surveillance As the market enjoys steady growth, storage vendors want to participate and have done so with a number of repurposed, repackaged, remarketed microSD cards targeted for video surveillance but with not much robustness, performance or capabilities specific to the application. Adding the absence of mean-time between failure (MTBF) specifications to the equation, microSD card reliability is typically a perceived measurement -- measured in hours of operation and relatively vague and hidden under metrics associated with the camera’s resolution and compression ratio. Therefore, when selecting a microSD card for surveillance cams at the edge, the choice should include a vendor that is trusted, has experience and a proven storage portfolio in video surveillance, and in microSD card technologies. Endurance, as it relates to microSD cards, represents the number of rewrites possible before the card can no longer store data correctly  High endurance Endurance, as it relates to microSD cards, represents the number of rewrites (program/erase cycles) that are possible before the card can no longer store data correctly. The rewrite operation is cyclical whereby a new stream of footage replaces older content by writing over it until the card is full, and the cycle repeats. The higher the endurance, the longer the card will perform before it needs to be replaced. Endurance is also referred to in terabytes written (TBW) or by the number of hours that the card can record continuously (while overwriting data) before a failure will occur. Health monitoring Health monitoring is a desired capability that not many microSD cards currently support and enables the host system to check when the endurance levels of a card are low and needs to be replaced. Having a card that supports this capability enables system integrators and operators with the ability to perform preemptive maintenance that will help to reduce system failures, as well as associated maintenance costs. Performance To capture continuous streams of raw footage, microSD cards within surveillance cams perform write operations about seventy to ninety percent of the time, whereas reading captured footage is performed about ten to thirty percent. The difference in read/write performance is dependent on whether the card is used in an artificial intelligent (AI) capable camera, or a standard one.   microSD cards deployed within surveillance cameras should support temperature ranges from -25 degrees Celsius to 85 degrees Celsius Finding a card that is write-friendly, and can provide enough bandwidth to properly capture streamed data, and is cost-effective, requires one that falls between fast industrial card capabilities and slower commercial ones. Bandwidth in the range of 50 MB/sec for writes and 80 MB/sec for reads are typical and sufficient for microSD cards deployed within surveillance cameras. Temperature ranges Lower capacity support of 32GB can provide room to attract the smaller or entry-level video surveillance deployments As microSD cards must be designed for continuous operation in extreme weather conditions and a variety of climates, whether located indoors or out, support for various temperature ranges are another consideration. Given the wide spectrum of temperatures required by the camera makers, microSD cards deployed within surveillance cameras should support temperature ranges from -25 degrees Celsius to 85 degrees Celsius, or in extreme cases, as low as -40 degrees Celsius. Capacity Selecting the right-sized capacity is also very important as there needs to be a minimum level to ensure that there is enough room to hold footage for a number of days or weeks before it is overwritten or the connectivity to the NVR is restored. Though 64GB is considered the capacity sweet spot for microSD cards deployed within surveillance cameras today, lower capacity support of 32GB can provide room to attract the smaller or entry-level video surveillance deployments. In the future, even higher capacities will be important for specific use cases and will potentially become standard capacities as the market evolves. When choosing the right storage microSD card to implement into your video surveillance system, make sure the card is designed specifically for the application – does it include the right levels of endurance and performance to capture continuous streams – can it withstand environmental challenges and wide temperature extremes – will it enable preventative and preemptive maintenance to provide years of service? It is critical for the surveillance system to be able to collect video footage whether the camera is connected to an NVR or is a standalone camera as collecting footage at the base of the surveillance system is the most crucial point of failure. As such, failsafe mechanisms are required to keep the camera recording until the network is restored.

Digital Watchdog (DW™), the industry leader in digital recorders, surveillance cameras and related management software, announces the release of the latest update for DW Spectrum™ IPVMS. The new v2.5 software adds powerful new features including a bookmarks engine and storage backup, performance improvements and improved support for multi-sensor cameras. Easy approach to HD surveillance DW Spectrum IPVMS is an elegantly easy cutting-edge approach to HD surveillance, addressing the primary obstacles and limitations of managing enterprise-level HD video while offering the lowest total cost of deployment and ownership of any solution on the market. The software offers advanced search features to help you quickly find incidents or instantly review an exact date and time. The software is cross-platform, installs quickly and provides instant network mapping and discovery to start viewing your entire security system in minutes. DW Spectrum IPVMS is a perfect solution for any application that requires ease, speed, efficiency, and unprecedented image quality. “With the timely release of DW Spectrum 2.5, it continues to deliver unprecedented return on investment without the need for an annual maintenance agreement.” said Patrick Kelley, Director of IP Sales – North America, Digital Watchdog. “These new features are seamlessly incorporated into the DW Spectrum user interface, providing a dramatically easier user experience for all user levels than any other video management solution on the market.” DW Spectrum™ IPVMS features: New Mobile Apps - Brand new cross-platform mobile apps for iOS and Android with better performance, revamped design and consistent user experience across both Apple iOS and Google Android devices. Bookmarks - Users can create bookmarks for specific segments of archived video with names, descriptions, and tags either manually or via the Rules & Events engine. Storage Backup - Users can now set up scheduled or real-time archive backup of high-res, low-res, or all streams from selected cameras to local, NAS, or even cloud-based storage locations (e.g. ftp sites, Amazon Prime Storage, etc). PTZ Preset as an Action - Users can now set up rules to trigger a PTZ preset as an action (e.g. create a motion event on a fixed camera that triggers the PTZ to look at the spot where motion occurred). Alarm Layout - allows users to set display-camera-on-Alarm-Layout as an action (aka pop-up video on alarm). Live Video Text Overlay - allows users to create custom text overlays-as-an-action (including Generic Event API). New device support: Axis F44 Main Unit Axis Q6000E panoramic camera ACTi V23 4 Channel Encoder (with I/O Support) Messoa IP Cameras (with I/O Support) Hikvision DS-6704HFI 4 Channel Video Server Implemented "Advanced" Settings for DWC-BVI2IR cameras Improved support for DWC-PZV2M72T cameras Vista VK2-1080XVRDPTPMF camera now mapped for advanced PTZ Arecont RTSP camera support implemented Digital I/O support for Arecont cameras implemented Improvements to management software: Added "Connect" button to the "Test" dialogue during dialogue Updated viewing cell icons and improved styling for "info" data Calendar widget can now be pinned Added layout background support for video wall Transcoding is now enabled by default for video export from multi-sensor cameras (e.g. DWC-PZV2M72T) Added warning before export if export will result in video downscaling (applies only to very high resolution or multi-sensor cameras) "Timeline mode" renamed to "Time Mode". Change can be seen in timeline, timestamps in Event Logs, Audit Trail and Bookmark Log Added ability to sort Alarm/Event Rules by any column Improved "Server Settings" and "System Administration" dialogs - now changes can be applied without closing dialog Storage Analytics improvement - storage utilised by deleted/moved out cameras is displayed separately Storage usage optimisations and improvements Improved time synchronisation mechanism between Servers Improved error messages for some cases of push updates failure "Free Storage Space" is increased from 5Gb to 50Gb for NAS   Server SSL can now be disabled API documentation is improved and extended General Server stability improvements based on anonymous usage and crash statistics

The shopping District, or Shoutengai, in Koto-Ku, Tokyo is located next to a busy train station that gathers many restaurants, shops, and sake bars together in this newly vibrant neighbourhood. As the area becoming more commercially alive, there was an increasing concern for crimes taken place within the district, especially during late nights. For the purpose of crime prevention, the Shopping District Association began to consider deploying surveillance cameras at the crime hot spots as a way to strengthen the public safety in the neighbourhood. Yet with its limited finances, the association had to look for an effective solution at a lower cost that would cope with its tight budget. Solution With the help from MESSOA, more than 60 Maven series NCR365 IR bullet cameras and a dozen of NOD385 IR dome cameras were introduced in this project. Equipped with a 3MP CMOS sensor and removable IR-cut filter for 24/7 monitoring, these cameras deliver exceptional image quality for accurate identification at crime-prone spots. Both cameras support onboard memory with microSD card slot that utilises edge recording technology. Up to 64GB of HD quality CCTV footage can be recorded locally on each camera without the need of installing NVRs, saving a significant amount of installation costs. Structure-wise, both the NCR365 and NOD385 are IP67 certified, featuring built-in heater and weatherproof enclosure to ensure the cameras are capable of withstanding any harsh environments in Japan, particularly in winter times. Result By utilising the edge-based recording technology, the whole project eliminated the need for a centralised recording server or storage, helping the association saving a great deal of costs as planned. This solution did not compromise reliability thanks to SD card’s solid state without any moving parts like a hard drive. The files can be securely stored and easily retrieved when needed. The motorised lens with auto focus of the NCR35 provided benefits to the project as well by drastically facilitating the time-consuming installation. Technicians were able to get the job done fast with easy by taking advantage of remote configuration with simple mouse clicks. The simplified set up cut the installation process short and saved lots of labour costs as result.

Partnership comes fresh on heels of Messoa’s exhibition of its latest HD IP cameras specifically designed for ALPR at IFSEC 2015 PlateSmart automatic license plate recognition (ALPR) software-only solutions are known for working with cameras across the spectrum of the security industry. Messoa, one of the security industry’s premier video camera manufacturers, has consistently produced cameras that work effectively with PlateSmart’s products, making for a strong and productive association. The relationship between the two companies has now been cemented by recently joint announcement that PlateSmart and Messoa have entered into an official integration partnership agreement. Messoa’s HD IP cameras for ALPR applications at IFSEC 2015 The announcement comes fresh on the heels of Messoa’s successful exhibition at IFSEC 2015 in London, where it displayed its latest models of high-definition IP cameras specifically designed for ALPR use. Among the cameras on display was a model boasting 3 megapixels of resolution in addition to headlight suppression, built-in infrared (IR) illumination, and other features conducive to excellent ALPR performance. Pairing of PlateSmart’s HD ALPR engine with Messoa’s HD cameras In addition to the high-quality product offerings from both companies, the effectiveness of the PlateSmart-Messoa combination springs from the pairing of PlateSmart’s high-definition (HD) ALPR engine with Messoa’s HD cameras, which enables users to cover a wider area and capture multiple plates with only a single camera. According to PlateSmart CEO John Chigos, the partnership with Messoa fits perfectly with the Company’s market philosophy. “PlateSmart has always made a point of choosing best-of-breed channel partners,” he says. “With Messoa, we are able to offer our customers everything they could want in license plate recognition. Messoa gives them a fantastic video image while PlateSmart gives them our unparalleled accuracy, flexibility, and data integrity protection.” “We are very excited about the partnership with PlateSmart”, said Allan Lee, President of MESSOA Technologies Inc. “With the integration of MESSOA CatchAll™ engine and PlateSmart HD LPR engine, it makes the state-of-art LPR solution obtainable and affordable, which ultimately benefits to the public safety as well as increases management effectiveness for applications in need of LPR solution such as parking lots, campus and schools, for example.”