Cormac Brick, Director of Machine Intelligence for Intel's Movidius Group, presents the "The Battle Between Traditional Algorithms and Deep Learning: The 3 Year Horizon" tutorial at the May 2017 Embedded Vision Summit.
Deep learning techniques are gaining in popularity for many vision tasks. Will they soon dominate every facet of embedded vision? Cormac Brick from Intel's Movidius Group explores this question by examining the theory and practice of applying deep learning to real-world vision problems, with examples illustrating how this shift is happening more quickly in some areas and more slowly in others.
Today it’s widely accepted that deep learning techniques involving convolutional neural networks (CNNs) are dominating for image recognition tasks. Other vision tasks use hybrid approaches. And others are still using classical vision techniques. These themes are further explored through a range of real-world examples: gesture tracking, which is moving to CNNs; SLAM, moving toward a hybrid approach; ISP (imaging pipelines), sticking with traditional algorithm; and geometry-based functions such as warping and point cloud manipulation, which will likely stay with classical approaches. The audience will gain a better understanding of the mix of approaches required to deploy a robust real-world embedded vision system.
