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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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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. |
format | Online Article Text |
id | pubmed-6443174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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