Intelligent security system development is progressing quickly. The usage of dashboard cameras, body cameras, Internet of Things (IoT) devices, and sensors in novel contexts are all examples of intelligent video surveillance systems that contribute to a safer and more secure environment. In fact, according to IDC, the global market for IP security cameras alone will reach around $50 billion in value by 2025.

Particularly for those that provide round-the-clock monitoring from intelligent surveillance cameras, the intricate camera networks that support these smart solutions already require a sizable quantity of storage. Storage requirements will further rise as networks and cameras proliferate. As the demand for dependable onboard storage develops, the availability of 4K capable cameras has also contributed to rising storage needs. Businesses need to understand the storage requirements at the core of the developing smart video surveillance business and how to appropriately satiate these expanding demands.

AI and expanded capability

Increased use of artificial intelligence (AI) in camcorders is a result of the desire for devices that can record video in better resolutions. Computers can now gather, examine, and research data gleaned through motion detection, object identification, and other technologies thanks to artificial intelligence.

High-definition resolutions like 4K, also known as Ultra High Definition, are necessary for AI-enabled capabilities (UHD). The more inferences may be made from a film, the more precise and comprehensive it is. In comparison to HD, 4K video requires significantly more storage, which has an impact on both the capacity and the rate at which video files must be recorded.

Four times as many pixels make up 4K footage as HD. Additionally, 8 bits, 10 bits, and 12 bits per channel, or 24, 30, or 36 bits per pixel, are supported by 4K-compatible video. Similar results can be obtained with HD images, with more colour when utilizing 24 bits per pixel or less colour when using 10 or 12 bits. In comparison to 1080p video, 4K video generates up to 5.7 times more information overall. For both video creation and surveillance, large video files create new demands on the infrastructure that powers these activities, including storage.

They frequently need more features, performance, and workload options to be AI-ready for advanced AI-enabled DVRs, video analytics servers, and deep learning systems. These needs can be satisfied by specially created storage systems.

Advancing the periphery

Similar to how audio and video data are being captured, gathered, and analysed, edge computing is becoming more and more significant. Previously, businesses and organizations relied on the public cloud’s adoption to centrally house massive data, including video. However, there has recently been a shift toward processing data closer to its creation, at the network’s edge.

Since quick data transmission and analysis are essential for efficient, dynamic, and timely public safety, latency is a key component of intelligent security systems. Taking pattern recognition in the present time mode as an illustration. Significant delays may occur if data must be sent back to a centralized data centre hundreds of miles distant for processing. This is especially true for video surveillance installations and rooms where footage is continuously recorded. AI analysis may proceed much more quickly by storing and analysing pertinent data at the edge, resulting in safer communities, effective operations, and smarter infrastructure.

Constant communication

Intelligent security solutions are needed in almost any scenario with 24/7 operations. But occasionally, the underlying hardware and software can go down. So it’s crucial that storage solutions have a backup procedure to guarantee ongoing operation or the capacity to restore data in the event of a malfunction.

Consider a hospital as an illustration, which has a centralized IP video recorder connected to hundreds of inside and outdoor cameras. The video cannot be captured if the Internet is down, posing a risk to the safety of the patients and the employees. In the event of a malfunction, these cameras can utilize microSD cards to guarantee ongoing recording. In order to ensure that the video stream is watched in chronological sequence without missing data, AI-powered software solutions can “repair” missing data streams with the content written to the card.

Storage systems must be robust to resist such conditions because smart security solutions frequently function in inclement weather. In large-scale systems and enclosures with operating vibration, such as high-density video analytics servers and cloud solutions, NAS storage must deliver enterprise-class functionality and dependability to safeguard data integrity.

Enterprise-class drives can reliably store and transfer tens of thousands of hours of training video, which speeds up the development of deep learning and AI algorithms. 

How can a storage system stay current with technology

As we all know, artificial intelligence and edge computing can continuously analyse high-resolution video, which can continuously process to better secure people and places. These two trends are currently creating intelligent security. Businesses can play a role by actively managing the associated storage and data infrastructure to deliver the most dependable performance, performance, and capacity required for every smart video surveillance use case.

When designing an intelligent video surveillance plan, businesses should put data management and storage front and centre, not as a secondary concern. AI workloads rise in tandem with increasing video resolution, and edge computing’s rapid data analysis makes it indispensable. Businesses must make sure their data infrastructure is prepared.