AI, Smart Video Promise To Extract Greater Insights From Security Video

Complex, extensive camera networks will already require a large amount of data storage, particularly if this is 24/7 monitoring from smart video-enabled devices.

AI, Smart Video Promise To Extract Greater Insights From Security Video

With 4K-compliant cameras projected to make up over 24% of all network cameras shipped by 2023 – there is a fast-growing desire for reliable storage on-board security cameras. The question for businesses is: do they look to break up their existing smart video network, by separating and compartmentalising cameras to handle data requirements, or do they increase its storage capabilities?

As some people begin to venture out and return to work following initial COVID-19 measures, we are also seeing demand for thermal imaging technology increase. New technology like this combined with more of these always-on systems being rolled out, means organisations will need to carefully consider their smart video strategy. Newer edge computing will play an important role in capturing, collecting, and analysing data and there are some key trends you can expect to see as a result of this evolution.

There are many more types of cameras being used today, such as body cameras, dashboard cameras, and new Internet of Things (IoT) devices and sensors. Video data is so rich nowadays, you can analyse it and deduce a lot of valuable information in real-time, instead of post-event.

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Edge computing and smart security

As public cloud adoption grew, companies and organisations saw the platform as a centralised location for big data. However, recently there’s been opposition to that trend. Instead we are now seeing data processed at the edge, rather than in the cloud. There is one main reason for this change in preference: latency.

Latency is an important consideration when trying to carry out real-time pattern recognition. It’s very difficult for cameras to process data – 4K surveillance video recorded 24/7 – if it has to go back to a centralised data centre hundreds of miles away. This data analysis needs to happen quickly in order to be timely and applicable to dynamic situations, such as public safety. By storing relevant data at the edge, AI inferencing can happen much faster. Doing so can lead to safer communities, more effective operations, and smarter infrastructure.

UHD and storage

AI-enabled applications and capabilities, such as pattern recognition, depend on high-definition resolutions such as 4K – also known as Ultra High Definition (UHD). This detailed data has a major impact on storage – both the capacity and speeds at which it needs to be written, and the network. Compared to HD, 4K video has much higher storage requirements and we even have 8K on the horizon.

As we know, 4K video has four times the number of pixels as HD video. In addition, 4K compliant video supports 8, 10, and 12 bits per channel that translate to 24-, 30- or 36-bit colour depth per pixel. A similar pattern holds for HD — more colour using 24 bits or less colour using 10 or 12 bits in colour depth per pixel. Altogether, there is up to a 5.7x increase in bits generated by 4K vs. 1080 pixel video. Larger video files place new demands on data infrastructure for both video production and surveillance. Which means investing in data infrastructure becomes a key consideration when looking into smart security.

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Always-on connectivity

Whether designing solutions that have limited connectivity or ultra-fast 5G capabilities, most smart security solutions need to operate 24/7, regardless of their environment. Yet, on occasion, the underlying hardware and software systems fail. In the event of this, it is important to establish a failover process to ensure continued operation or restore data after a failure, including everything from traffic control to sensors to camera feeds and more.

Consider the example of a hospital with dozens or even over a hundred cameras connected to a centralised recorder via IP. If the Ethernet goes down, no video can be captured. Such an event could pose a serious threat to the safety and security of hospital patients and staff. For this reason, microSD cards are used in cameras to enable continuous recording. Software tools – powered by AI – can then “patch” missing data streams with the content captured on the card to ensure the video stream can be viewed chronologically with no content gaps.

Thermal imaging

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Health and safety is the number one priority for all organisations as people return to work and public spaces. Some organisations are deploying thermal imaging to help screen individuals for symptoms as they return. Organisations that operate with warehouses, depots and assembly lines will traditionally have large amounts of cameras located outside of the entrance. With thermal imaging smart video in place, these cameras can now serve a dual purpose as a screening device. The thermal imaging technology is capable of detecting elevated body temperatures, with 10-25 workers being scanned in one shot, from one camera – making it an efficient and accurate process. This way, staff can use the information to help identify people who may need further screening, testing, and/or isolation before returning to work.

While this may not increase data storage requirements, it can change your retention policies and practices.

Smart security today is about utilising AI and edge computing, to deliver an always-on, high-resolution video provision that can help keep people safe 24/7. These trends increase the demands and importance of monitoring, which means requirements of the supporting data infrastructure improve to match that, including the ability to proactively manage the infrastructure to help ensure reliable operation. Companies need to make sure they have considered all the storage and policy challenges as part of their smart security strategy for the future. 

This news was originally published at sourcesecurity.com

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