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Discovery and recognition of motion primitives in human activities

We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which captures the motion variation of a group of s...

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Detalles Bibliográficos
Autores principales: Sanzari, Marta, Ntouskos, Valsamis, Pirri, Fiora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443174/
https://www.ncbi.nlm.nih.gov/pubmed/30933990
http://dx.doi.org/10.1371/journal.pone.0214499
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author Sanzari, Marta
Ntouskos, Valsamis
Pirri, Fiora
author_facet Sanzari, Marta
Ntouskos, Valsamis
Pirri, Fiora
author_sort Sanzari, Marta
collection PubMed
description We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis.
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spelling pubmed-64431742019-04-17 Discovery and recognition of motion primitives in human activities Sanzari, Marta Ntouskos, Valsamis Pirri, Fiora PLoS One Research Article We present a novel framework for the automatic discovery and recognition of motion primitives in videos of human activities. Given the 3D pose of a human in a video, human motion primitives are discovered by optimizing the ‘motion flux’, a quantity which captures the motion variation of a group of skeletal joints. A normalization of the primitives is proposed in order to make them invariant with respect to a subject anatomical variations and data sampling rate. The discovered primitives are unknown and unlabeled and are unsupervisedly collected into classes via a hierarchical non-parametric Bayes mixture model. Once classes are determined and labeled they are further analyzed for establishing models for recognizing discovered primitives. Each primitive model is defined by a set of learned parameters. Given new video data and given the estimated pose of the subject appearing on the video, the motion is segmented into primitives, which are recognized with a probability given according to the parameters of the learned models. Using our framework we build a publicly available dataset of human motion primitives, using sequences taken from well-known motion capture datasets. We expect that our framework, by providing an objective way for discovering and categorizing human motion, will be a useful tool in numerous research fields including video analysis, human inspired motion generation, learning by demonstration, intuitive human-robot interaction, and human behavior analysis. Public Library of Science 2019-04-01 /pmc/articles/PMC6443174/ /pubmed/30933990 http://dx.doi.org/10.1371/journal.pone.0214499 Text en © 2019 Sanzari et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sanzari, Marta
Ntouskos, Valsamis
Pirri, Fiora
Discovery and recognition of motion primitives in human activities
title Discovery and recognition of motion primitives in human activities
title_full Discovery and recognition of motion primitives in human activities
title_fullStr Discovery and recognition of motion primitives in human activities
title_full_unstemmed Discovery and recognition of motion primitives in human activities
title_short Discovery and recognition of motion primitives in human activities
title_sort discovery and recognition of motion primitives in human activities
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443174/
https://www.ncbi.nlm.nih.gov/pubmed/30933990
http://dx.doi.org/10.1371/journal.pone.0214499
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