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Living on the edge

Jeremy Krinitt, VP Enterprise Business Development

I recently read an analysis from IHS indicating that the demise of server-based analytics is imminent. The analysis leaves one with an inevitable sense that all analytics will be done at the edge. They provide additional analysis that “pure server only based analytics was estimated to have shrunk by $39.4 million in one year… However, more steady transition, along with a continued dominance of server-based analytics in certain environments was still forecast.” And here’s a second however from IHS: “demand for high end server-based analytics is expected to be sustained.” True, technologies in the enterprise arena such as motion detection and applications like directional motion are being built into cameras and efficiently processed on the camera. In the residential space, this is often the case as well. More and more residential solutions are heading to the cloud and the ability to trigger recording or notification locally based on simple analytics provides many benefits. Yet even in the residential space, advanced analytics running in the cloud are dominant. Clearly, there are apparent advantages to edge analytics, offering better system scalability and access to uncompressed data. However again, in reality most analytics are still server-based. In fact, server-based video analytics offers high reliability, strong processing capabilities and sophisticated search options. Furthermore, server-based video analytics enables organizations to apply various types of analytical applications, providing value across all levels of the organization. So, I ask myself, will better edge processing capabilities and architecture “terminate” server-based video analytics? If you, for one, think this is the grand finale of server-based analytics, think again. Or read the round table discussion on the continuing role for server-based video analytics. Edge-based video analytics applications are normally limited to one application per camera due to insufficient processing power. While more powerful processors are going into camera and other edge devices, implemented in this fashion you are looking at a fixed capability device installed on the edge versus a more flexible infrastructure that can be centrally managed and expanded through additional servers or cloud resources. Server-based video analytics is not as limited by a device’s processing power, and in addition is camera agnostic. This means organizations can enjoy the ability to run multiple video analytics applications at the same time, and on any type of camera. Confused? Check out this great summary table of pros and cons for server-based vs. edge-based video analytics. Server-based, edge-based; what is evident is the growing need for smart and reliable video analytics solutions, way beyond what anyone once imagined video analytics could deliver. And this need will grow even more as the use of metadata becomes more common in security and video applications. I know metadata is a significant topic on its own, and will rightfully demand dedicated blog posts in the future. Stay tuned…

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