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A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique

Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) “skeleton” joints on the human body at 3...

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Autores principales: Niu, Jianwei, Wang, Xiai, Wang, Dan, Ran, Linghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070687/
https://www.ncbi.nlm.nih.gov/pubmed/32085653
http://dx.doi.org/10.3390/s20041119
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author Niu, Jianwei
Wang, Xiai
Wang, Dan
Ran, Linghua
author_facet Niu, Jianwei
Wang, Xiai
Wang, Dan
Ran, Linghua
author_sort Niu, Jianwei
collection PubMed
description Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) “skeleton” joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect.
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spelling pubmed-70706872020-03-19 A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique Niu, Jianwei Wang, Xiai Wang, Dan Ran, Linghua Sensors (Basel) Article Microsoft Kinect, a low-cost motion capture device, has huge potential in applications that require machine vision, such as human-robot interactions, home-based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three-dimensional (3D) “skeleton” joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect. MDPI 2020-02-18 /pmc/articles/PMC7070687/ /pubmed/32085653 http://dx.doi.org/10.3390/s20041119 Text en © 2020 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Niu, Jianwei
Wang, Xiai
Wang, Dan
Ran, Linghua
A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique
title A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique
title_full A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique
title_fullStr A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique
title_full_unstemmed A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique
title_short A Novel Method of Human Joint Prediction in an Occlusion Scene by Using Low-Cost Motion Capture Technique
title_sort novel method of human joint prediction in an occlusion scene by using low-cost motion capture technique
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070687/
https://www.ncbi.nlm.nih.gov/pubmed/32085653
http://dx.doi.org/10.3390/s20041119
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