New Vision AI platform upgrade includes analytics enhancements

New York, NY (USA)

Ooso's latest release of its Vision AI software platform includes significant video analytics enhancements for video surveillance hardware and access control systems.

The version release is accompanied by the company’s introduction of its proprietary edge device, the new Onpoint touchless reader tablet. This output allows security teams to further reduce organisational risks associated with outdated physical security systems, better manage controlled access, and identify safety threats.

Oosto is harnessing the power of edge computing, facial recognition, and Vision AI to bring physical security into the modern age. Edge computing in smart video surveillance offers many advantages including improved response times, a smaller physical footprint (because fewer servers are required for video processing), improved scalability, and even built-in failover so that if a network failure occurs, edge devices continue operating without pause.

With Oosto’s latest edge enhancement, the Vision AI platform offers a “no-compromise” solution that de-risks video analytics at the edge while maintaining high performance and accuracy. Oosto leverages state-of-the-art Kubernetes technologies to protect its algorithms by containerizing the application (vs. running the neural networks directly on edge devices).

Performance-wise, Oosto’s neural networks have been optimised to support low-power edge devices while still offering superior performance and recognition accuracy. Based on internal benchmarks, Oosto’s algorithms are now seven times more efficient in terms of watts-per-video stream than the company’s next nearest rival.

“It was only a matter of time before real-time video surveillance and access control systems would harness the power of the edge,” said Dieter Joecker, Chief Technology Officer, Oosto. “With our recent enhancements to our Vision AI platform, Oosto is transferring compute workloads from expensive on-premise servers to the edge but, perhaps more significantly, we’re doing this without sacrificing recognition accuracy, security and performance in real-world scenarios or expanding the physical footprint of the system.”

Many organisations are increasingly moving critical processes from central servers to a variety of edge devices and its adoption is growing across industries, use cases and geographies. In fact, the global edge computing market is expected to grow to $155.9 billion (USD) by 2030, a compound annual growth rate of 38.9% (source: Research and Markets, September 2022). This shift enables edge devices such as smart cameras, gates and access control systems with local decision-making to open gates for authorised employees or send real-time alerts to security teams when a person-of-interest enters a commercial space.


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