“Combining CNNs and Conventional Algorithms for Low-Compute Vision: A Case Study in the Garage,” a Presentation from the Chamberlain Group

Nathan Kopp, Principal Software Architect for Video Systems at the Chamberlain Group, presents the “Combining CNNs and Conventional Algorithms for Low-Compute Vision: A Case Study in the Garage” tutorial at the September 2020 Embedded Vision Summit.

Chamberlain Group (CGI) is a global leader in access control solutions with its Chamberlain and LiftMaster garage door opener brands and myQ connected technology. In this presentation, you’ll learn how CGI is innovating to bring efficient, affordable computer vision into the garage, opening new possibilities and insights for homeowners and businesses.

With constant improvements in neural network architectures and advancements in low-power edge processors, it is tempting to assume that CNNs will solve every vision problem. However, simpler “conventional” computer vision techniques continue to offer an attractive cost-to-performance ratio and require orders of magnitude less training data. Unfortunately, these algorithms often need hand-tuning of parameters, and do not generalize well to previously unseen environments. By combining CNNs with simpler algorithms into a layered, intelligent vision pipeline—and by understanding the constraints of the problem—the weaknesses of simpler algorithms can be offset by the strengths of CNNs, while still preserving their cost-saving benefits.

See here for a PDF of the slides.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.



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