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On-device Real-time Hand Gesture Recognition

2021-10-29 18:33:25
George Sung, Kanstantsin Sokal, Esha Uboweja, Valentin Bazarevsky, Jonathan Baccash, Eduard Gabriel Bazavan, Chuo-Ling Chang, Matthias Grundmann
arXiv_CV
       
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).

Abstract (translated)

URL

https://arxiv.org/abs/2111.00038

PDF

https://arxiv.org/pdf/2111.00038.pdf

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