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Hardware Accelerated Image Processing on an FPGA-SoC Based Vision System for Closed Loop Monitoring and Additive Manufacturing Process Control Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Towards a Professional Gesture Recognition with RGB-D from Smartphone

Authors : Pablo Vicente Moñivar, Sotiris Manitsaris, Alina Glushkova

Published in: Computer Vision Systems

Publisher: Springer International Publishing

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Abstract

The goal of this work is to build the basis for a smartphone application that provides functionalities for recording human motion data, train machine learning algorithms and recognize professional gestures. First, we take advantage of the new mobile phone cameras, either infrared or stereoscopic, to record RGB-D data. Then, a bottom-up pose estimation algorithm based on Deep Learning extracts the 2D human skeleton and exports the 3rd dimension using the depth. Finally, we use a gesture recognition engine, which is based on K-means and Hidden Markov Models (HMMs). The performance of the machine learning algorithm has been tested with professional gestures using a silk-weaving and a TV-assembly datasets.

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previous chapter Hybrid Geometric Similarity and Local Consistency Measure for GPR Hyperbola Detection
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Metadata
Title
Towards a Professional Gesture Recognition with RGB-D from Smartphone
Authors
Pablo Vicente Moñivar
Sotiris Manitsaris
Alina Glushkova
Copyright Year
2019
Book
Computer Vision Systems

Print ISBN: 978-3-030-34994-3

Electronic ISBN: 978-3-030-34995-0

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-34995-0_22

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