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Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images
The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presen...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9102319/ https://www.ncbi.nlm.nih.gov/pubmed/35591221 http://dx.doi.org/10.3390/s22093531 |
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author | Ryselis, Karolis Blažauskas, Tomas Damaševičius, Robertas Maskeliūnas, Rytis |
author_facet | Ryselis, Karolis Blažauskas, Tomas Damaševičius, Robertas Maskeliūnas, Rytis |
author_sort | Ryselis, Karolis |
collection | PubMed |
description | The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground–background masks from depth images for human body segmentation. The framework can be used to speed up the manual depth image annotation process with no semantics known beforehand and can apply segmentation using a performant algorithm while the user only adjusts the parameters, or corrects the automatic segmentation results, or gives it hints by drawing a boundary of the desired object. The approach has been tested using two different datasets with a human in a real-world closed environment. The solution has provided promising results in terms of reducing the manual segmentation time from the perspective of the processing time as well as the human input time. |
format | Online Article Text |
id | pubmed-9102319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91023192022-05-14 Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images Ryselis, Karolis Blažauskas, Tomas Damaševičius, Robertas Maskeliūnas, Rytis Sensors (Basel) Article The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground–background masks from depth images for human body segmentation. The framework can be used to speed up the manual depth image annotation process with no semantics known beforehand and can apply segmentation using a performant algorithm while the user only adjusts the parameters, or corrects the automatic segmentation results, or gives it hints by drawing a boundary of the desired object. The approach has been tested using two different datasets with a human in a real-world closed environment. The solution has provided promising results in terms of reducing the manual segmentation time from the perspective of the processing time as well as the human input time. MDPI 2022-05-06 /pmc/articles/PMC9102319/ /pubmed/35591221 http://dx.doi.org/10.3390/s22093531 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ryselis, Karolis Blažauskas, Tomas Damaševičius, Robertas Maskeliūnas, Rytis Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images |
title | Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images |
title_full | Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images |
title_fullStr | Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images |
title_full_unstemmed | Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images |
title_short | Computer-Aided Depth Video Stream Masking Framework for Human Body Segmentation in Depth Sensor Images |
title_sort | computer-aided depth video stream masking framework for human body segmentation in depth sensor images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9102319/ https://www.ncbi.nlm.nih.gov/pubmed/35591221 http://dx.doi.org/10.3390/s22093531 |
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