Sep 23–26, 2019

Handtrack.js: Building gesture-based interactions in the browser using TensorFlow.js

Victor Dibia (Cloudera Fast Forward Labs)
1:15pm1:55pm Thursday, September 26, 2019
Location: 3B - Expo Hall

Who is this presentation for?

Data scientists, front end web (javascript developers) developers, User experience designers.

Level

Intermediate

Description

While JavaScript continues to be the most used programming language (Github Octoverse 2018 Report), until recently, there has been limited frameworks for machine learning that cater to this audience. With the advent of Tensorflow.js – a library for developing and training ML models in JavaScript, for deployment in browser or on Node.js – things are changing. When running inside the browser, Tensorflow.js utilizes the GPU of the device via WebGL to enable fast parallelized floating point computation. In Node.js, TensorFlow.js binds to the TensorFlow C library, enabling full access to TensorFlow.

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 cover an end-to-end example on my experience building 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 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. A live demo prototyped using Handtrack.js will be shown.

Prerequisite knowledge

General familiarity with Machine Learning, and Javascript.

What you'll learn

Understand the state for Machine Learning in the Browser using Tensorflow.js 1.0 Walkthrough 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. Cover examples of hand tracking interactions prototyped in the browser, UX concerns and future possibilities.
Photo of Victor Dibia

Victor Dibia

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. He holds an M.S. from Carnegie Mellon University and a Ph.D. from City University of Hong Kong.

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