Handtrack.js: Building Gesture-based Interactions in the Browser Using Tensorflow.js
In the browser, Machine Learning can enable truly novel forms of interactions, while reaping the benefits associated with on-device computation, such as reduced latency for interactive applications, reduced model distribution costs, and enhanced privacy, as data is no longer sent to remote servers for analysis.
In this talk, I provide an overview of the Tensorflow.js library, benchmark performance results for image tasks in the browser, and provide a live demonstration of Handtrack.js – a library for prototyping real time hand tracking interactions. Handtrack.js is powered by an object detection neural network (MobilenetV2, SSD) and allows users predict the location (bounding box) of human hands in an image, video, or canvas html tag. The talk will also cover steps and best practices for deploying a neural network model in the browser – from data collection, model training, model conversion to Tensorflow.js webmodel format, model hosting, and inference.
Prerequisite knowledgeNone, come as you are.
What you'll learn
Cloudera Fast Forward Labs
Victor Dibia is a Research Engineer with Cloudera’s Fast Forward Labs where his work focuses on prototyping state of the art machine learning algorithms and advising clients. Prior to this, he was a Research Staff Member at the IBM TJ Watson Research Center, New York. His research interests are at the intersection of human computer interaction, computational social science, and applied AI. A senior member of IEEE, Victor has published work at venues like the AAAI Conference on Artificial Intelligence and ACM Conference on Human Factors in Computing Systems. His work has been featured in outlets such as the Wall Street Journal and VentureBeat. He holds an M.S. from Carnegie Mellon University and a Ph.D. from City University of Hong Kong.
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