Network Video Recorders (NVR) / Network DVRs (89)
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Video surveillance systems are producing more unstructured data than ever before. A dramatic decrease in camera costs in recent years has led many businesses to invest in comprehensive surveillance coverage, with more cameras generating more data. Plus, advances in technology mean that the newest (8K) cameras are generating approximately 800% more data than their predecessors (standard definition). Traditional entry-level solutions like network video recorders (NVRs) simply aren’t built to handle massive amounts of data in an efficient, resilient and cost-effective manner. This has left many security pioneers grappling with a data storage conundrum. Should they continue adding more NVR boxes? Or is there another, better, route? Retaining video data In short, yes. To future proof their video surveillance infrastructure, an increasing number of businesses are adopting an end-to-end surveillance architecture with well-integrated, purpose-built platforms for handling video data through its lifecycle. This presents significant advantages in terms of security, compliance and scalability, as well as unlocking new possibilities for data enrichment. All of this with a lower total cost of ownership than traditional solutions. Security teams would typically delete recorded surveillance footage after a few days or weeks Previously, security teams would typically delete recorded surveillance footage after a few days or weeks. However, thanks to increasingly stringent legal and compliance demands, many are now required to retain video data for months or even years. There’s no doubt that this can potentially benefit investigations and increase prosecutions, but it also puts significant pressure on businesses’ storage infrastructure. Data lifecycle management This necessitates a more intelligent approach to data lifecycle management. Rather than simply storing video data in a single location until it’s wiped, an end-to-end video surveillance solution can intelligently migrate data to different storage platforms and media as it ages. So, how does this work? Video is recorded and analysed on a combination of NVR, hyperconverged infrastructure (HCI) and application servers. Then, it’s moved to resilient file storage for a pre-determined period, where it can be immediately retrieved and accessed for review. Finally, based on policies set by heads of security, data is moved from file storage to highly secure, low-cost archive storage such as an object, tape or cloud. Data is moved from file storage to highly secure, low-cost archive storage Long-term storage This process is known as tiering. It allows businesses to use reliable, inexpensive long-term storage for most of their data, whilst still enabling security pioneers to retrieve video data when the need arises, such as during a compliance audit, or to review footage following a security breach. In a nutshell, it offers them the best of both worlds. Scaling your video surveillance infrastructure can be a headache. Businesses that rely on NVRs – even high-end units with 64 or even 96 hard drives – are finding themselves running out of capacity increasingly quickly. In order to scale, security pioneers then have to procure new boxes. With NVRs, this inevitably involves a degree of guesswork. Should they go for the largest possible option, and risk over provisioning? Or perhaps a smaller option, and risk running out of capacity again? Common management console Security pioneers can easily add or remove storage capacity or compute resources – separately or together As businesses add new cameras or replace existing ones, many end up with inadequate surveillance infrastructure made up of multiple NVR boxes along with several application servers for running other surveillance functions such as access control, security photo databases, analytics, etc. This patchwork approach leaves security pioneers scrambling for capacity, maintaining various hardware footprints, repeating updates and checks across multiple systems, and taking up valuable time that could be better spent elsewhere. By contrast, flexible HCI surveillance platforms aggregate the storage and ecosystem applications to run on the same infrastructure and combine viewing under a common management console, avoiding ‘swivel chair’ management workflows. Plus, they offer seamless scalability. Security pioneers can easily add or remove storage capacity or compute resources – separately or together. Data storage solutions Over time, this ensures a lower total cost of ownership. First and foremost, it removes the risk of over provisioning and helps to control hardware sprawl. This in turn leads to hardware maintenance savings and lower power use. Many security pioneers are now looking beyond simple data storage solutions for their video surveillance footage. Meta tags can provide context around data, making it easier to find and access when needed Instead, they’re asking themselves how analysing this data can enable their teams to work faster, more efficiently and productively. Implementing an end-to-end video surveillance architecture enables users to take advantage of AI and machine learning applications which can tag and enrich video surveillance data. These have several key benefits. Firstly, meta tags can provide context around data, making it easier to find and access when needed. Object storage platform For instance, if security teams are notified of a suspicious red truck, they can quickly find data with this tag, rather than manually searching through hours of data, which can feel like looking for a needle in a haystack. Plus, meta tags can be used to mark data for future analysis. This means that as algorithms are run over time, policies can be set to automatically store data in the right location. For example, if a video is determined to contain cars driving in and out of your premises, it would be moved to long-term archiving such as an object storage platform for compliance purposes. If, on the other hand, it contained 24 hours of an empty parking lot, it could be wiped. These same meta tags may be used to eventually expire the compliance data in the archive after it is no longer needed based on policy. Video surveillance architecture Continuing to rely on traditional systems like NVRs will fast become unsustainable for businesses Even if your organisation isn’t using machine learning or artificial intelligence-powered applications to enhance your data today, it probably will be one, three, or even five years down the line. Implementing a flexible end-to-end video surveillance solution prepares you for this possibility. With new advances in technology, the quantity of data captured by video surveillance systems will continue rising throughout the coming decade. As such, continuing to rely on traditional systems like NVRs will fast become unsustainable for businesses. Looking forward, when moving to an end-to-end video surveillance architecture, security pioneers should make sure to evaluate options from different vendors. For true futureproofing, it’s a good idea to opt for a flexible, modular solution, which allow different elements to be upgraded to more advanced technologies when they become available.
Cloud-based technology can reduce IT costs, streamline application management and make infrastructure more flexible and scalable. So, it’s no surprise that cloud video surveillance solutions (also known as video surveillance as a service or VSaaS) are gaining momentum in a big way. In fact, according to recent reports, the VSaaS market is forecasted to increase at a compound annual growth rate of 10.4% by 2025. But some company owners may wonder – what services does a cloud model deliver and is such a solution right for my business? This article aims to help you determine what cloud video surveillance solution is right for your business and the benefits you can enjoy if you decide to deploy a VSaaS solution. Full cloud-based recording vs cloud-managed First, a bit of clarification on cloud video surveillance models, as definitions can vary from provider to provider. A full cloud-based recording solution is one in which both video recording and management are done offsite (for example, cameras streaming directly to the cloud). While this model can be a good option for some, many large enterprise businesses simply don’t have the bandwidth capacity or network resources required to upload all of their videos to the cloud. Even with the bandwidth capacity, this can be a cost-prohibitive model when hundreds or thousands of IP cameras are involved. A full cloud-based recording solution is one in which both video recording and management are done offsite But that doesn’t mean enterprise businesses can’t take advantage of cloud-managed video surveillance. With this solution, video recording and storage happen on your premises (with network video recorders (NVRs) or a video management system (VMS)), but the video management aspect is handled in the cloud by a third-party provider, usually as a subscription-based service. The provider hosts the central video server overseeing your on-premises devices. Some providers also allow you to back up portions of the video to the cloud, so you can store and share video evidence or select clips needed for investigations. This model combines the performance benefits of local recording with the convenience and cost savings of the cloud. Centralised video surveillance solution Perhaps the greatest benefit of using this type of cloud-managed video surveillance solution is centralisation. Because all of your devices are centrally managed in the cloud, you don’t have to travel to a distant location to update a recorder or camera’s software – it’s all done remotely by the provider from a central location. This can save you both time and money, especially since it’s necessary to consistently monitor the configuration settings on cameras and NVRs to ensure they’re correct and functioning properly. If your hardware malfunctions and it isn’t detected immediately, instances of lost video can occur. And business owners know that losing video evidence of theft or fraud could have significant consequences to the efficiency and effectiveness of an investigation. Round the clock monitoring Some providers monitor for changes in cameras’ field of view, so if a camera is blocked or moved, you’ll be alerted With a cloud-managed model, you can rest assured that if a camera goes down or another technical issue arises, the provider will know and will handle it immediately so you can avoid unnecessary truck rolls, which can be costly. Some providers will even monitor for changes in your cameras’ field of view, so if a camera is blocked or moved, you’ll be alerted right away. This type of around-the-clock monitoring eliminates your need for an in-house data centre and the IT staff necessary to maintain the video system. This is particularly important if you don’t have the infrastructure or the personnel to host your own video networking equipment. You can also save time with the deployment of your video surveillance solution since your provider will get your system up and running quickly. There’s no need to worry about setting up or configuring the central server or any application software – it’s all taken care of by your provider. Flexible and cost-effective In many cloud-managed solutions, you can also skip the large upfront capital cost of a video surveillance investment and pay a monthly fee for all of these services. This is particularly helpful if it’s difficult for your organisation to make large capital investments. You may or may not have to invest in onsite devices (cameras and NVRs), depending on the provider you choose. Some providers will allow you to finance your hardware, while others will want you to purchase it upfront. Many cloud providers also offer robust web clients for viewing video and conducting investigations remotely Many cloud providers also offer robust web clients for viewing video and conducting investigations remotely. These do not require any local downloads, which saves you time and money by avoiding the need for additional IT resources. It also alleviates worrying about whether or not you have the latest version, as the clients are automatically updated. And if you don’t want to spend a lot of time on video analysis, some cloud-managed models offer predefined reports on what’s most important to you. For example, a list of potentially suspicious transactions matched with video – so you can quickly scan to investigate. Getting the right solution Determining whether a cloud-managed video surveillance solution is right for your business is a big decision involving many factors, including your business’s size, bandwidth, and network infrastructure, and overall budget for physical security. By considering the points above, the hope is that you can more easily determine which model is best for your business.
The evolution of smart video technology continues at pace. As in many other industries, the onset of the COVID-19 pandemic expedited timelines and the artificial intelligence (AI) video world is continuing its rapid evolution in 2021. As video demand and the use of AI to make sense of the visual data increase, the number of cameras and subsequent data produced are growing rapidly, and these are forcing the creation of new edge architectures. Cameras and AI in traffic management ‘Smart factories’ can leverage AI to detect flaws or deviations in the production line in real time In addition, a new generation of ‘smart’ use cases has developed. For example, in ‘smart cities’, cameras and AI analyse traffic patterns and adjust traffic lights, in order to improve vehicle flow, reduce congestion and pollution, and increase pedestrian safety. ‘Smart factories’ can leverage AI to detect flaws or deviations in the production line in real time, adjusting to reduce errors and implement effective quality assurance measures. As a result, costs can be greatly reduced through automation and earlier fault detection. Evolution of smart video The evolution of smart video is also happening alongside other technological and data infrastructure advancements, such as 5G. As these technologies come together, they’re impacting how we architect the edge. And, they’re driving a demand for specialised storage. Listed below are some of the biggest trends that we’re seeing: Greater volume means greater quality The volume and variety of cameras continue to increase with each new advancement, bringing new capabilities. Having more cameras allow more to be seen and captured. This could mean having more coverage or more angles. It also means more real-time video can be captured and used to train AI. Quality also continues to improve with higher resolutions (4K video and above) Quality also continues to improve with higher resolutions (4K video and above). The more detailed the video, the more insights can be extracted from it. And, the more effective the AI algorithms can become. In addition, new cameras transmit not just one video stream, but also additional low-bitrate streams used for low-bandwidth monitoring and AI pattern matching. Smart cameras operate 24/7 Whether used for traffic management, security or manufacturing, many of these smart cameras operate 24/7, 365 days a year, which poses a unique challenge. Storage technology must be able to keep up. For one thing, storage has evolved to deliver high-performance data transfer speeds and data writing speeds, to ensure high quality video capture. And, actual on-camera storage technology must deliver longevity and reliability, critical to any workflow. Real world context is vital to understanding endpoints Whether used for business, in scientific research or in our personal lives, we’re seeing new types of cameras that can capture new types of data. With the potential benefits of utilising and analysing this data, the importance of reliable data storage has never been more apparent. Considering context when designing storage technology As we design storage technology, we must take the context into consideration, such as location and form factor. We need to think of the accessibility of cameras (or lack thereof), are they atop a tall building or maybe amid a remote jungle? Such locations might also need to withstand extreme temperature variations. All of these possibilities need to be taken into account, so as to ensure long-lasting, reliable continuous recording of critical video data. Chipsets are improving artificial intelligence (AI) capability Improved compute capabilities in cameras means processing happens at the device level, enabling real-time decisions at the edge. New camera chipsets deliver enhanced AI capability We’re seeing new chipsets arrive for cameras that deliver improved AI capability, and more advanced chipsets add deep neural network processing for on-camera deep learning analytics. AI keeps getting smarter and more capable. Cloud must support deep learning technology Just as camera and recorder chipsets are coming with more compute power, in today’s smart video solutions most of the video analytics and deep learning is still done with discrete video analytics appliances or in the Cloud. To support these new AI workloads, the Cloud has gone through some transformation. Neural network processors within the Cloud have adopted the use of massive GPU clusters or custom FPGAs. They’re being fed thousands of hours of training video, and petabytes of data. These workloads depend on the high-capacity capabilities of enterprise-class hard drives (HDDs), which can already support 20TB per drive and high-performance enterprise SSD flash devices, platforms or arrays. Reliance on the network Wired and wireless internet have enabled the scalability and ease of installation that has fuelled the explosive adoption of security cameras, but it could only do so where LAN and WAN infrastructures already exist. 5G technology aids camera installations Emerging cameras that are 5G-ready are being designed to load and run 3rd party applications 5G removes many barriers to deployment, allowing expansive options for placement and ease of installation of cameras at a metropolitan level. With this ease of deployment comes new greater scalability, which drives use cases and further advancements in both camera and cloud design. For example, cameras can now be standalone, with direct connectivity to a centralised cloud, as they’re no longer dependent on a local network. Emerging cameras that are 5G-ready are being designed to load and run 3rd party applications, which can bring broader capabilities. Yet with greater autonomy, these cameras will need even more dynamic storage. They will require new combinations of endurance, capacity, performance, and power efficiency, to be able to optimally handle the variability of new app-driven functions. Paving the way for the edge storage revolution It’s a brave new world for smart video, and it is as complex as it is exciting. Architectural changes are being made to handle new workloads and prepare for even more dynamic capabilities at the edge and at end points. At the same time, deep learning analytics continue to evolve at the back end and the Cloud. Understanding workload changes, whether at the camera, recorder, or the Cloud level, is critical to ensuring that new architectural changes are augmented by continuous innovation in storage technology.
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