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Robust Arm and Hand Tracking by Unsupervised Context Learning

Hand tracking in video is an increasingly popular research field due to the rise of novel human-computer interaction methods. However, robust and real-time hand tracking in unconstrained environments remains a challenging task due to the high number of degrees of freedom and the non-rigid character...

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Detalles Bibliográficos
Autores principales: Spruyt, Vincent, Ledda, Alessandro, Philips, Wilfried
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168488/
https://www.ncbi.nlm.nih.gov/pubmed/25004155
http://dx.doi.org/10.3390/s140712023
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author Spruyt, Vincent
Ledda, Alessandro
Philips, Wilfried
author_facet Spruyt, Vincent
Ledda, Alessandro
Philips, Wilfried
author_sort Spruyt, Vincent
collection PubMed
description Hand tracking in video is an increasingly popular research field due to the rise of novel human-computer interaction methods. However, robust and real-time hand tracking in unconstrained environments remains a challenging task due to the high number of degrees of freedom and the non-rigid character of the human hand. In this paper, we propose an unsupervised method to automatically learn the context in which a hand is embedded. This context includes the arm and any other object that coherently moves along with the hand. We introduce two novel methods to incorporate this context information into a probabilistic tracking framework, and introduce a simple yet effective solution to estimate the position of the arm. Finally, we show that our method greatly increases robustness against occlusion and cluttered background, without degrading tracking performance if no contextual information is available. The proposed real-time algorithm is shown to outperform the current state-of-the-art by evaluating it on three publicly available video datasets. Furthermore, a novel dataset is created and made publicly available for the research community.
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spelling pubmed-41684882014-09-19 Robust Arm and Hand Tracking by Unsupervised Context Learning Spruyt, Vincent Ledda, Alessandro Philips, Wilfried Sensors (Basel) Article Hand tracking in video is an increasingly popular research field due to the rise of novel human-computer interaction methods. However, robust and real-time hand tracking in unconstrained environments remains a challenging task due to the high number of degrees of freedom and the non-rigid character of the human hand. In this paper, we propose an unsupervised method to automatically learn the context in which a hand is embedded. This context includes the arm and any other object that coherently moves along with the hand. We introduce two novel methods to incorporate this context information into a probabilistic tracking framework, and introduce a simple yet effective solution to estimate the position of the arm. Finally, we show that our method greatly increases robustness against occlusion and cluttered background, without degrading tracking performance if no contextual information is available. The proposed real-time algorithm is shown to outperform the current state-of-the-art by evaluating it on three publicly available video datasets. Furthermore, a novel dataset is created and made publicly available for the research community. MDPI 2014-07-07 /pmc/articles/PMC4168488/ /pubmed/25004155 http://dx.doi.org/10.3390/s140712023 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Spruyt, Vincent
Ledda, Alessandro
Philips, Wilfried
Robust Arm and Hand Tracking by Unsupervised Context Learning
title Robust Arm and Hand Tracking by Unsupervised Context Learning
title_full Robust Arm and Hand Tracking by Unsupervised Context Learning
title_fullStr Robust Arm and Hand Tracking by Unsupervised Context Learning
title_full_unstemmed Robust Arm and Hand Tracking by Unsupervised Context Learning
title_short Robust Arm and Hand Tracking by Unsupervised Context Learning
title_sort robust arm and hand tracking by unsupervised context learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168488/
https://www.ncbi.nlm.nih.gov/pubmed/25004155
http://dx.doi.org/10.3390/s140712023
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