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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.
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