PROMOTED CONTENT
THE FUTURE OF SMART SECURITY
BARBER BRINKMAN, SENIOR BUSINESS DEVELOPMENT MANAGER, EMEAI SMART VIDEO, WESTERN DIGITAL, DISCUSSES HOW THE RISING DEMANDS FOR AI- AND 4K-ENABLED SMART VIDEO CAMERAS ARE DRIVING THE NEED FOR RELIABLE AND ROBUST STORAGE SOLUTIONS.
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mart security is advancing rapidly across the UAE. As AI and 4K rise in adoption on smart video cameras, these higher video resolutions are driving the demand for more data to be stored on-camera. Complex, extensive camera networks already require a large amount of data storage, particularly if there is 24/7 monitoring from smart videoenabled devices. 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
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SEPTEMBER 2020
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 seeing demand increase for thermal imaging technology. New technology like this combined with more always-on systems being rolled out, mean organisations will need to carefully consider their smart video strategy. Previously, companies have preferred public cloud adoption. However, we are now seeing data processed at the edge, rather than in the cloud. One main reason for this change: latency.
EDGE COMPUTING AND SMART SECURITY Previously, companies have preferred public cloud adoption. However, we’re now seeing data processed at the edge, rather than in the cloud. One main reason for this change: 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
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