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Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons

Nowadays, the need for reliable and low-cost multi-camera systems is increasing for many potential applications, such as localization and mapping, human activity recognition, hand and gesture analysis, and object detection and localization. However, a precise camera calibration approach is mandatory...

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Detalles Bibliográficos
Autores principales: Romeo, Laura, Marani, Roberto, Perri, Anna Gina, D’Orazio, Tiziana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269787/
https://www.ncbi.nlm.nih.gov/pubmed/35808479
http://dx.doi.org/10.3390/s22134986
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author Romeo, Laura
Marani, Roberto
Perri, Anna Gina
D’Orazio, Tiziana
author_facet Romeo, Laura
Marani, Roberto
Perri, Anna Gina
D’Orazio, Tiziana
author_sort Romeo, Laura
collection PubMed
description Nowadays, the need for reliable and low-cost multi-camera systems is increasing for many potential applications, such as localization and mapping, human activity recognition, hand and gesture analysis, and object detection and localization. However, a precise camera calibration approach is mandatory for enabling further applications that require high precision. This paper analyzes the available two-camera calibration approaches to propose a guideline for calibrating multiple Azure Kinect RGB-D sensors to achieve the best alignment of point clouds in both color and infrared resolutions, and skeletal joints returned by the Microsoft Azure Body Tracking library. Different calibration methodologies using 2D and 3D approaches, all exploiting the functionalities within the Azure Kinect devices, are presented. Experiments demonstrate that the best results are returned by applying 3D calibration procedures, which give an average distance between all couples of corresponding points of point clouds in color or an infrared resolution of 21.426 mm and 9.872 mm for a static experiment and of 20.868 mm and 7.429 mm while framing a dynamic scene. At the same time, the best results in body joint alignment are achieved by three-dimensional procedures on images captured by the infrared sensors, resulting in an average error of 35.410 mm.
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spelling pubmed-92697872022-07-09 Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons Romeo, Laura Marani, Roberto Perri, Anna Gina D’Orazio, Tiziana Sensors (Basel) Article Nowadays, the need for reliable and low-cost multi-camera systems is increasing for many potential applications, such as localization and mapping, human activity recognition, hand and gesture analysis, and object detection and localization. However, a precise camera calibration approach is mandatory for enabling further applications that require high precision. This paper analyzes the available two-camera calibration approaches to propose a guideline for calibrating multiple Azure Kinect RGB-D sensors to achieve the best alignment of point clouds in both color and infrared resolutions, and skeletal joints returned by the Microsoft Azure Body Tracking library. Different calibration methodologies using 2D and 3D approaches, all exploiting the functionalities within the Azure Kinect devices, are presented. Experiments demonstrate that the best results are returned by applying 3D calibration procedures, which give an average distance between all couples of corresponding points of point clouds in color or an infrared resolution of 21.426 mm and 9.872 mm for a static experiment and of 20.868 mm and 7.429 mm while framing a dynamic scene. At the same time, the best results in body joint alignment are achieved by three-dimensional procedures on images captured by the infrared sensors, resulting in an average error of 35.410 mm. MDPI 2022-07-01 /pmc/articles/PMC9269787/ /pubmed/35808479 http://dx.doi.org/10.3390/s22134986 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
Romeo, Laura
Marani, Roberto
Perri, Anna Gina
D’Orazio, Tiziana
Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons
title Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons
title_full Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons
title_fullStr Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons
title_full_unstemmed Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons
title_short Microsoft Azure Kinect Calibration for Three-Dimensional Dense Point Clouds and Reliable Skeletons
title_sort microsoft azure kinect calibration for three-dimensional dense point clouds and reliable skeletons
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269787/
https://www.ncbi.nlm.nih.gov/pubmed/35808479
http://dx.doi.org/10.3390/s22134986
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