NVIDIA Metropolis provides AI cities with edge-to-cloud platform for video analytics

NVIDIA unveiled Monday its NVIDIA Metropolis intelligent video analytics platform that aims to makes cities safer and smarter by applying deep learning to video streams for applications such as public safety, traffic management and resource optimization. Over 50 NVIDIA AI city partner companies are already providing products and applications that use deep learning on GPUs, many of which will be on display this week at the GPU Technology Conference.

NVIDIA Metropolis is the foundation of the AI City—an edge-to-cloud platform that can turn anonymized video into valuable insights. Driven by technologies like NVIDIA Jetson TX2 at the edge and NVIDIA Tesla in the data center, it delivers video analytics for a range of applications. AI is changing how it captures, inspects and analyzes data. This impacts everything from public safety to traffic and parking management to law enforcement and city services. Metropolis gives users the tools, technologies, and support to address it all with smarter, faster applications.

Metropolis spans multiple NVIDIA products that operate on a unified architecture. High-performance deep learning inferencing happens at the edge with the NVIDIA Jetson embedded computing platform, and through servers and data centers with NVIDIA Tesla GPU accelerators.

“Deep learning is enabling powerful intelligent video analytics that turn anonymized video into real-time valuable insights, enhancing safety and improving lives,” said Deepu Talla, vice president and general manager of the Tegra business at NVIDIA. “The NVIDIA Metropolis platform enables customers to put AI behind every video stream to create smarter cities.”

Video is huge generator of data, captured by hundreds of millions of cameras deployed in areas such as government property, public transit, commercial buildings and roadways. By 2020, the cumulative number of cameras is expected to rise to approximately 1 billion. Humans currently monitor only a fraction of captured video, with most stored on disks for later review.

Initial efforts at real-time video analytics techniques have proved far less reliable than human interpretation. Intelligent video analytics solves this challenge by using deep learning in cameras, on-premises video recorders and servers, and in the cloud to monitor video instantaneously with accuracy and scalability.

Data visualization is powered by NVIDIA Quadro professional graphics, and the edge-to-cloud platform is supported by the company’s software development kits, including JetPack, DeepStream and TensorRT. Growing AI City Partner Support More than 50 NVIDIA AI city partners already help customers reveal insights and take real-time action using deep learning on NVIDIA GPUs. Among them are vendors such as Avigilon, Dahua, Hanwha Techwin, Hikvision and Milestone.

“With the fast-paced environment of a city, there are a near infinite number of activities taking place,” said Dr. Mahesh Saptharishi, chief technology officer at Avigilon. “We’re excited by the potential of NVIDIA’s Metropolis platform, as Avigilon continues to deliver AI-powered surveillance solutions and video analytics that focus users’ attention on what matters most, in order to take action.”

“NVIDIA’s end-to-end Metropolis platform can be applied to video streams to create smarter and safer applications for a variety of industries — from transportation to commercial,” said Shiliang Pu, president at Hikvision Research Institute. “The benefit of GPU deep learning is that data can be analyzed quickly and accurately to drive deeper insights.”

“City management customers using Milestone’s upcoming Video Processing Server with NVIDIA Metropolis are positioned to take the lead in the adoption of deep learning for video-enabled IoT devices,” said Bjørn Skou Eilertsen, chief technology officer at Milestone Systems. “Unleashing the value of this metadata will provide intelligent insights to take smart action.”

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