In order to pave the way for the creation of AI cities, NVIDIA has officially unveiled NVIDIA Metropolis edge-to-cloud platform for Video Analytics. Metropolis aims at making cities safer and smarter by applying deep learning to video streams for applications like public safety, traffic management, and resource optimization. More than 50 NVIDIA AI city partner companies are already providing products and applications that use deep learning on GPUs. Many of these will be showcased this week at the GPU Technology Conference.

 

Videos generate the largest amount of data in the world captured by millions of cameras deployed in areas like government property, commercial buildings, roadways, etc. It is estimated that by the year 2020, the cumulative number of cameras is going to rise to approximately 1 Billion. 

 

 

Currently, humans 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 addresses this issue by using deep learning in cameras, on-premises video recorders and servers, and in the cloud to monitor video instantaneously with accuracy and scalability.

 

NVIDIA Jetson™ embedded computing platform, and servers, as well as, data centers with NVIDIA® Tesla® GPU accelerators lead towards high-performance deep learning inferencing. Rich data visualization is powered by NVIDIA Quadro® professional graphics. The entire edge-to-cloud platform is supported by NVIDIA’s rich software development kits, including JetPack, DeepStream, and TensorRT™.

 

 

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots and self-driving cars that can perceive and understand the world.