Smart video and the use of artificial intelligence has accelerated the video surveillance sector. It enables organisations to collate information and use/analyse it in the right way
By 2025 the global market for video surveillance cameras will grow to nearly $50 billion, according to the latest estimates by IDC. As the demand for smart video grows, paralleled by an increase in the use of Artificial Intelligence (AI), this will drive the development of data architectures at the edge and smart cities.
AI is excellent at doing specific, narrow tasks incredibly well. The aim of AI is not to teach technology to see the world as humans do, but instead to enable computers to capture, analyse and learn about the human world, in rapid and accurate ways. The profound value of AI comes from taking computer intelligence capabilities – such as object recognition, movement detection and tracking or counting objects/persons – and using these in the right application. Given the utility of these applications, it’s not surprising that the amalgamation of video, artificial intelligence and sensor data is a hotbed for new services across industries and integral to the adoption of smart cities globally. Brian Mallari, Director of Product Marketing, Smart Video, Western Digital, explores exclusively for Security Buyer UK how smart video surveillance is integral to the running of an efficient urban environment.
Larger and smarter use cases
Smart video is a keystone of modern security and surveillance activity. However, it should also be recognised that the market is expanding through a growing amount of use cases. These include medical applications, sports analysis, factories, traffic management and even agricultural drones.
Intelligent technology is making these use cases “smart”, i.e. devices deploying intelligent insights. For example, in “smart cities”, cameras and AI analyse traffic patterns and adjust traffic lights accordingly to improve vehicle flow, reduce congestion and pollution and increase pedestrian safety. Another example of this is “smart factories”, which implement the type of narrow tasks AI excels in, such as detecting flaws or deviations in the production line in real-time and adjusting to reduce errors. Smart cameras can be very effective in this use case at the level of quality assurance, keeping costs down through automation and earlier fault detection.
“Artificial Intelligence (AI) will drive the development of data architectures at the edge and smart cities”
As smart video evolves, it’s developing in parallel to other technological and data infrastructure advancements, such as 5G. As these technologies come together, they’re impacting the architecture of the edge, and what we require from data storage. More specifically, they’re driving a demand for specialised storage. Here are some of the biggest trends currently developing:
- Strength in numbers
Having more cameras means there is more media-rich data to be captured and analysed. This means it can be used to train AI.
Simultaneously, cameras are supporting higher resolutions (4K video and above). The more detailed and sharp the video, the more insights can be extracted from it, and thus the more effective the AI algorithms can become. In addition, new cameras transmit not just a main video stream but also additional low-bitrate streams, perfect for low-bandwidth monitoring and AI pattern matching.
Some of the biggest challenges for these types of workloads is the fact that they’re always on. Especially necessary in the case of security, many smart cameras operate 24/7, 365 days a year, as is needed for their role.
“The more detailed and sharp the video, the more insights can be extracted from it”
The challenge here is that storage technology must be able to keep up. One way in which storage has evolved to meet this challenge, is the development of the ability to deliver high performance data transfer speeds and data writing speed, to ensure high quality video capture. Furthermore, on-camera storage technology that can deliver longevity and reliability has become even more critical, in comparison to storage technology at a remote data centre.
- The rich variety of endpoints
The realm of security relies on more than just visual data. New types of cameras are being developed with new types of data to be analysed. Cameras can be found everywhere – atop buildings, inside moving vehicles, in drones, and even in doorbells.
The location and form factor of smart cameras impacts the storage technology required. The accessibility of cameras (or lack thereof) needs to be considered – are they atop a tall building? Maybe amid a remote jungle? Such locations might need to withstand extreme temperature variations. For example, security drones monitoring a location of extreme heat. All of these possibilities need to be considered to ensure long-lasting, reliable continuous recording of critical video data.
- AI chipsets
Increasingly real-time decisions are being made at the edge at device level, due to improved compute capabilities in cameras. New chipsets are arriving for cameras that deliver improved AI capability, and more advanced chipsets offer deep neural network processing for on-camera deep learning analytics. AI keeps getting smarter and more capable.
As the innovation within cameras continues, there is a rising expectation that deep learning – requiring large video data sets in order to be effective – will happen on-camera too, driving the need for more primary on-camera storage.
“The majority of the video analytics and deep learning for today’s smart video solutions is completed by discrete video analytics”
Even for solutions that employ standard security cameras, AI-enhanced chipsets and discrete GPUs (graphic processing units) are still being used in network video recorders (NVR), video analytics appliances, and edge gateways to enable advanced AI functions and deep learning analytics. With NVR firmware and OS (operating system) architecture evolving to add such capabilities to mainstream recorders, the implications for storage are large, having to handle a much bigger workload.
For example, there is a need to go beyond just storing single and multiple camera streams. Today, metadata from real-time AI and reference data for pattern matching needs to be stored as well.
- Don’t say goodbye to the cloud
The majority of the video analytics and deep learning for today’s smart video solutions is completed by discrete video analytics appliances or in the cloud. Similarly, broader Internet of Things (IoT) applications that use sensor data beyond video are also tapping into the power of the deep learning cloud to create more effective, smarter AI.
To support these new AI workloads, the cloud has gone undergone a transformation. Neural network processors within the cloud have adopted the use of massive GPU clusters or custom FPGAs (field programmable gate array). They’re being fed thousands of hours of training video, and petabytes of data.
This cloud activity still requires specialised and robust storage: these workloads depend on the high-capacity capabilities of enterprise-class hard drives (HDDs) and high-performance enterprise SSD flash devices, platforms or arrays.
- Reaching for the stars with 5G
Wired and wireless internet have enabled the scalability and ease of installation that has driven the explosive adoption of security cameras – but it could only do so where LAN and WAN infrastructures already exist. However, 5G is a game changer here.
5G removes many barriers to deployment, allowing more options for where a camera can be installed and easily used in metropolitan locations. With this ease of deployment comes new greater scalability, which increases use cases and encourages further advancements in both camera and cloud design.
For example, cameras can now be stand-alone, no longer dependent on a local network and instead using direct connectivity to a centralised cloud. Emerging cameras that are already 5G-ready are being designed to load and run 3rd party applications that can bring broader capabilities. With 5G behind it, the sky’s the limit on smart video innovation.
The flipside is that with greater autonomy, comes the need for even more dynamic storage for these cameras. They will require new combinations of endurance, capacity, performance, and power efficiency to be able to handle the variability of new app-driven functions.
Storage technologies must not only keep pace with the already growing demands of smart video, but they must also enable and encourage new capabilities and smart use cases. As the proliferation of smart video throughout the security space only continues, the hidden storage complexities should not be forgotten. Smart video is integral to the development of the security industry to help benefit building technology, transportation, access control, public safety and infrastructure.
Scott McQuarrie, CEO of Backstreet Surveillance
Video surveillance is the most important strategic element of any facility security plan. As such, it is vital that the video system itself be secure. Backstreet Cyber Secure for example is NDAA government certified and uses zero technologies or components from unauthorised sources, ensuring it is free from hidden hardware or software backdoors or weak security portals. In addition, it essentially delivers every element the market has asked the surveillance industry to provide in a single platform. This includes advanced deep learning AI, P2P remote access, automated motion tracking, gunshot detection and location ID, 24hr color night vision video technology, facial detection, profile identification, people counting and much more.
Advanced video surveillance technology enables security personnel and company management to direct operations in new and innovative ways. For instance, in a retail setting, if more than five people are standing in a checkout line, a system can automatically call for additional workers. Additionally, facial detection makes it known when unauthorised individuals, vehicles, or even animals enter a facility, property or specific work area, which takes real-time security well beyond badges and access control cards.
A highlight of AI powered systems is the 360-degree camera. Advanced image processing software enables the arial fisheye view to be digitally adapted to 16 different viewing angles. The result is several clear 90-degree fields of view, providing maximum surveillance flexibility from a single camera. This camera is also equipped with two-way audio, which provides the ability to remotely interact with staff and visitors such as a lobby or with first responders in an emergency situation.
Hartmut Schaper, CEO Azena
The ability to run video analytics directly on the camera, at the edge of the network, is a growing trend in the market. What benefits does this bring in terms of technology, operational efficiency and potential cost savings?
The cameras we have access to today have a level of processing power once thought nearly impossible. Due to this increased power, analytics can be run directly on the edge – enabling the use of AI for more robust analytics and reducing the toll on network bandwidth previously consumed by constant video streams to central servers for further processing. This has enabled smart cameras to be used as a powerful, data-rich sensor capable of capturing all types of information. Video analysis on the edge also provides additional privacy safeguards, as the data being analysed – a face, a license plate or other identifier – can be configured to only be transmitted from the camera when an event is triggered, significantly reducing the amount of data – and potential personal information – that is required to transmit from the camera to a management platform.
Cameras are an extremely powerful sensor, capable of capturing rich data that can be analysed for any number of things – movement, sound, light, human behavior. Without AI-enabled apps and a sophisticated IoT infrastructure for device management support, the contribution of this raw data collected by the cameras would be far less valuable to an organisation.
Additionally, in the past with traditional cameras, new innovations in analytics need to be constantly re-engineered to work with the many different camera vendors in the market. With our open camera operating system, free to any participating camera vendor, innovators have to develop new applications only once to present it to a global audience and ensure that their applications can run on cameras from a variety of manufacturers. This results in end users who are able to pick and choose a solution that fits their needs best, without having to worry about proprietary technology.
Brian Mallari, Director of Product Marketing, Smart Video, Western Digital
By 2025 the global market for video surveillance cameras will grow to nearly $50 billion, according to the latest estimates by IDC. As the demand for smart video grows, paralleled by an increase in the use of Artificial Intelligence (AI), this will drive the development of data architectures at the edge and smart cities.
AI is excellent at doing specific, narrow tasks incredibly well. The aim of AI is not to teach technology to see the world as humans do, but instead to enable computers to capture, analyse and learn about the human world, in rapid and accurate ways. The profound value of AI comes from taking computer intelligence capabilities – such as object recognition, movement detection and tracking or counting objects/persons – and using these in the right application. Given the utility of these applications, it’s not surprising that the amalgamation of video, artificial intelligence and sensor data is a hotbed for new services across industries and integral to the adoption of smart cities globally.
Smart video is a keystone of modern security and surveillance activity. However, it should also be recognised that the market is expanding through a growing amount of use cases. These include medical applications, sports analysis, factories, traffic management and even agricultural drones.
Intelligent technology is making these use cases “smart”, i.e. devices deploying intelligent insights. For example, in “smart cities”, cameras and AI analyse traffic patterns and adjust traffic lights accordingly to improve vehicle flow, reduce congestion and pollution and increase pedestrian safety. Another example of this is “smart factories”, which implement the type of narrow tasks AI excels in, such as detecting flaws or deviations in the production line in real-time and adjusting to reduce errors. Smart cameras can be very effective in this use case at the level of quality assurance, keeping costs down through automation and earlier fault detection.
To stay up to date on the latest, trends, innovations, people news and company updates within the global security market please register to receive our newsletter here.
Media contact
Rebecca Morpeth Spayne,
Editor, Security Portfolio
Tel: +44 (0) 1622 823 922
Email: editor@securitybuyer.com
