Dallmeier Network Video Recorders (NVR) / Network DVRs

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.

By 2020, video surveillance using fixed, body and mobile cameras is expected to capture an astounding 859 PB of video daily. Increasing retention regulations and higher resolution cameras, are forcing the video surveillance industry to reassess its approach to data storage. Large capacity primary storage tends to be expensive to procure and costly to implement – especially without a sound architecture that can balance storage performance levels with the speed of access needed to recall video footage. Active archive strategy These challenges are thrusting storage tiers to the forefront of system design. Storage tiers in video surveillance had previously meant simply using a separate archive or attaching add-on capacity directly to network video recorders. Many of the new storage options designed for video surveillance are pulling together different storage tiers into a single storage architecture Many of the new storage options designed for video surveillance are pulling together different storage tiers (and in some cases storage media) into a single storage architecture, such as an active archive solution. This balance can be achieved with an active archive strategy that automates migration of data between different storage types, to ensure the data is on the correct storage type at the correct time to meet performance and retention requirements without blowing the budget. This approach also ensures ease of access while automatically moving content from more expensive tiers of storage to more cost-effective long-term tiers of storage. This allows for greater efficiencies in how recorded footage is treated throughout its lifecycle. In some cases, it includes moving data from edge devices to centralised storage, and then to the public cloud. Scalable video storage solutions As storage demands have increased, video management vendors have turned to storage specialists for solutions that can accommodate large numbers of high-resolution video files, metadata associated with the footage for easy searching, along with much needed scalable solutions. In terms of video management software, this means the integration of video content from different storage types, tiers and physical locations is required, and which considers the performance profile of each storage type. With an active archive solution, video content is searchable and accessible directly by the end users regardless of where it is stored. Deploying an active archive solution enables surveillance users to reduce the complexity and costs of managing data for long term retention As seen in many product categories, camera and storage vendors continue to provide extremely competitive offerings. But, storage-specific solutions for video surveillance have lagged behind the roadmaps for video equipment and, as more and more cameras have entered the market, less attention has been placed on video storage capacities. Tiered storage strategy The surveillance industry has evolved considerably from the days of the 8mm video recorder; however, enterprise storage solutions will be forced to evolve further to cope with changing storage retention requirements. Video storage is quickly becoming one of the most expensive parts in a surveillance solution, but there is hope. Deploying an active archive solution will enable surveillance users to reduce the complexity and costs of managing from terabytes to petabytes of data for long term retention. By finding a storage solution that delivers the ability to implement a tiered storage strategy, users can adhere to regulation requirements to retain video footage and meet their safety and security objectives, while also significantly reducing storage costs and operational expenses.

Digitalisation technologies promise great improvements in an enormous variety of logistics processes. German manufacturer Dallmeier is particularly well positioned for the combination of systems from video technology, sensor systems, data management and intelligent use of elements of AI. At transport logistic 2019 in Munich, from June 4-7, 2019, Dallmeier will present a broad portfolio of solutions especially for customers in general cargo logistics at Stand 620, Hall A3. Dallmeier's customer base also includes the very largest logistics corporations. Logistics management systems The German manufacturer Dallmeier can look back on more than 35 years of experience in the development of cameras, recording systems and software. Solutions for customers in the logistics sector represent a primary focus of the company's corporate strategy. The portfolio includes systems for protecting property, entrance and exit areas, claims management, yard management, and a broad range of logistics management systems from real-time localisation of unit goods up to automatic volume calculation. A very recent development is their cooperation with the SAP integrator T.CON A very recent development is their cooperation with the SAP integrator T.CON. The solutions developed jointly by the two companies enable the transmission of a wide variety of valuable business data straight from video systems into SAP ERP systems and address major challenges in the supply chain, HR and compliance area. SAP standard objects To date, the cooperation between Dallmeier and T.CON has produced two solutions for the supply chain area: The ‘Digital Gate’ automates vehicle registration and consignment notes management with a self-service portal running on SAP Fiori. The system recognises vehicle classes, registration numbers, ID numbers and hazardous substance categories. Depending on the requirement, the system can be integrated in yard management and hazardous substance handling functions. The freight data in SAP is supplemented with the optically acquired data using SAP standard objects. Accordingly, it can be integrated directly in SAP TM or LE-TRA (ECC 6.0). The ‘unit good measurement’ solution enable freight items to be measured and weighed automatically by video systems and wireless weighing forks, and the data can be integrated in SAP EWM or WM. Perimeter protection The many advantages of this innovation range from the optimal use of load capacities to plausibility checks and up to coordinated warehouse storage and retrieval strategies. For perimeter protection, Dallmeier combines its patented Panomera® camera technology with a two-tier object classification system using neural networks. This places customers in the position of being able to reduce the number of false alarms to practically zero This places customers in the position of being able to reduce the number of false alarms to practically zero. At the same time, the role of the cameras is changed so not only do they function as a verification system, they can also assume most of the responsibility for incident detection, and consequently fewer systems are needed to guarantee effective perimeter protection. Optimised vehicle control The combination of the Dallmeier video technology and the proprietary, modular process management software with a sensor system offers logistics companies very many advantages. Most significant among these are systems for real-time localisation of unit goods, permanently solving the problem of misplaced or lost packages, which in many medium-size firms happens to between five and ten percent of all items handled every day. With the Dallmeier system, the position of every package is known from the moment it is received until the moment it is shipped. A similar system also enables uninterrupted package tracking for large logistics businesses and privacy-compliant theft investigation among other capabilities. Other solutions on display at the Dallmeier stand are concerned with optimising the efficiency of all kinds of processes, such as improved yard management and optimised vehicle control, e.g., through the display of loading levels, automatic gate assignment or even optimised claims management.

Hardly any other topic is creating as much excitement as Artificial Intelligence (AI) at the moment. High expectations and extravagant promises abound, particularly in the field of video security technology: Here, the ideas about what it can do range from detecting unusual behaviours such as attacks on individuals to recognising individual faces even in large crowds of people to automatic detection of the proverbial ‘bomb in a suitcase’. The Regensburg-based company Dallmeier has been working on and with AI technologies for years, and has now published four practical statements intended to help customers and providers to make a realistic assessment of AI. Video security technology People often ignore the fact that new technologies always require public debate and changes to very real framework conditions At the beginning of a hype cycle, when innovations are being introduced, people often ignore the fact that new technologies always require public debate and changes to very real framework conditions before they can be implemented wholesale. The still unresolved problem in autonomous driving – when it comes to accidents where the car has to make potentially fatal decisions – has become an almost classic example. There are similar unresolved questions when AI is used in video security technology: How much freedom to decide should a system be given? What quality criteria will be established for detecting objects, for example? Who is to be held accountable when an attack is not detected, for example, even though the expectation may possibly exist already among the people? What reaction times will be defined, by when must response teams reach the site in the event of an ‘AI alarm’? Are there even enough personnel available for the potential new intervention and search options? How are the many ‘false positives’ to be handled when facial recognition is used to find a suspect, for example? Video analysis systems Technical systems are becoming more and more complex. This is why it is essential to evaluate all of the parameters that affect the performance of a whole solution. The IT axiom ‘garbage in, garbage out’ is most apposite in this context: Neural networks for classifying objects or processes or good facial recognition software can only deliver results that are consistent with the quality of the video image they receive: AI-based video analysis systems can only be as good as the camera systems that capture the images for them. In this context, it will be particularly important to be able to define and plan minimum picture qualities properly in all parts of the video image, plan camera angles correctly, and consider many other details. And the person behind the system must be also be included in the overall consideration with regard to qualification and organisational questions. In short: Unless all factors are tuned to work together, it will not be possible to ensure compliance with standards – which by the way have not even been defined yet! Perimeter protection Initial deployment scenarios and functioning solutions already exist, whether it be in the optimisation and analysis of analogue processes With all due caution: It goes without saying that Artificial Intelligence will play a decisive role in video technology – or may even become a core component of the discipline. Initial deployment scenarios and functioning solutions already exist, whether it be in the optimisation and analysis of analogue processes, e.g., at a casino gaming table, in the improved classification of objects for perimeter protection, or in the assisted tracking of individuals in the context of urban surveillance. The key point in all of these systems: Today and probably for a long time to come a human is still at the centre – the operator, the policeman, the forensic specialist. And it is for these functions that AI in video technology now already delivers useful assistance systems. They are being improved rapidly and take over tedious, error-prone tasks. But contrary to all the advertising features on YouTube, automatic location of a planted ‘suitcase bomb’ in complex circumstances is still well beyond current technological capabilities. Technical innovation Every technical innovation is predestined to contend with ambiguous definitions, exaggerated expectations and variable interpretations of its capabilities: No one ‘really knows’, but everyone involved has an opinion. This is why it is important to examine and question closely: Which functions are market-ready and implementable – even if a little tweaking is needed –, and what is still purely in the realm of research? Particularly with a view to strategic decisions and investments, prospective users should always begin by asking themselves whether a given result can be expected in twelve months, five years, or ever. Otherwise, they run the risk of losing sight of obvious solutions to pressing problems.

Fraud, high operating costs and scarce business intelligence data – these are the challenges the industry pioneer Dallmeier addressed with its ‘Smart Casino Solutions’ at ICE London 2019. Dallmeier’s combination of video technology and artificial intelligence (AI) to improve profitability of the three essential casino areas – gaming, surveillance, and marketing – led to an extremely positive response from ICE visitors. Dallmeier’s Casino Automation Technology (CAT) is the first gaming automation system that is live in a productive environment. It is currently available for Baccarat and Blackjack and uses AI technologies and standard Full HD IP cameras to recognise chips and cards, provide real-time information about bet position and bet value, game pace and float value etc. CAT allows for a highly increased game pace, a much more effective protection from fraud and an accurate player rating CAT allows for a highly increased game pace, a much more effective protection from fraud and an accurate player rating, to name just a few key functionalities. Combining CCTV technology with AI Konrad Hechtbauer, managing director of Dallmeier International, states: “We were especially happy to hear from our visitors that they appreciate our approach to build on established, existing technology – i.e. CCTV – and combine it with artificial intelligence applications. With CAT, we open up a treasure of data that finally allow them to make much more informed decisions to maximise their profitability regarding table games.” Many casinos still install huge numbers of PTZ and fisheye cameras, creating a more and more complex environment with questionable image quality and security effect. With the new 360-degree version of Dallmeier patented Panomera camera technology, casino operators can cover much larger areas with the same number of operators while at the same time significantly improving overall security due to a 360-degree, high resolution view of the overall scene. In case of an incident, multiple operators can zoom in at the same time resolving complex scenarios – but without losing the overall picture both in live and recording as it is the case with PTZ cameras. Achieving security goals at lower cost With our new 360-degree solution, customers achieve their security goals better at a lower cost" Konrad Hechtbauer states: “Despite all the great options that AI-based gaming automation technologies like our CAT system offer, casinos still have to fulfil their requirements for classical security and safety. With our new 360-degree solution, customers achieve their security goals better at a lower cost. And at the same time lay the foundation to use the very same cameras as ‘optical data sensors’ for all sorts of business intelligence applications. Quite a few of our booth visitors told us that with Dallmeier’s new 360-degree-version of the Panomera camera technology, they finally see the chance to save their casinos money in a field that is commonly known as a ‘cost driver’.” Improving overall profitability By intelligently combining video technology-based functions (e.g. people counting, crowd analysis, face recognition and many more) with AI based gaming automation technologies and other analysis techniques, casinos can significantly improve their overall profitability. Konrad Hechtbauer explains: “Gathering, analysing and understanding those data help casinos better plan their business and make more informed decisions based on the behaviour and preferences of their visitors and players.”