Bookmark and Share

"Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitoring," a Presentation from Camio

Register or sign in to access the Embedded Vision Academy's free technical training content.

The training materials provided by the Embedded Vision Academy are offered free of charge to everyone. All we ask in return is that you register, and tell us a little about yourself so that we can understand a bit about our audience. As detailed in our Privacy Policy, we will not share your registration information, nor contact you, except with your consent.

Registration is free and takes less than one minute. Click here to register, and get full access to the Embedded Vision Academy's unique technical training content.

If you've already registered, click here to sign in.

See a sample of this page's content below:


Carter Maslan, CEO of Camio, presents the "Blending Cloud and Edge Machine Learning to Deliver Real-time Video Monitoring" tutorial at the May 2017 Embedded Vision Summit.

Network cameras and other edge devices are collecting ever-more video – far more than can be economically transported to the cloud. This argues for putting intelligence in edge devices. But the cloud offers unique, valuable capabilities, such as aggregating information from multiple cameras, applying state-of-the-art algorithms, and providing users with access to their data anywhere, any time.

Camio uses a combination of machine learning at the edge (in network cameras and network video recorders) and in the cloud to generate alerts, highlight the most significant events captured by a camera, and to let users search for events of interest. In this talk, Maslan explores the trade-offs between edge and cloud processing for systems that extract meaning from video, and explains how the two approaches can be combined to create big opportunities.