Year: 2021
Type: preprint
Abstract: We present an on-device real-time hand gesture recognition (HGR) system, which detects a set of predefined static gestures from a single RGB camera. The system consists of two parts: a hand skeleton tracker and a gesture classifier. We use MediaPipe Hands as the basis of the hand skeleton tracker, improve the keypoint accuracy, and add the estimation of 3D keypoints in a world metric space. We create two different gesture classifiers, one based on heuristics and the other using neural networks (NN).
Source: arXiv (Cornell University)
Authors: Sung, George, Sokal, Kanstantsin, Uboweja, Esha, Bazarevsky, Valentin, Baccash, Jonathan, Bazavan, Eduard Gabriel, Chang, Chuo-Ling, Grundmann, Matthias
Institution: Google (United States)
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3211295600