Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction
Tracking movements of the body in a natural living environment of a person is a challenging undertaking. Such tracking information can be used as a part of detecting any onsets of anomalies in movement patterns or as a part of a remote monitoring environment. The tracking information can be mapped a...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Telemedicine and Applications |
| Online Access: | http://dx.doi.org/10.1155/2021/5551753 |
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| author | Shahram Payandeh Jeffrey Wael |
| author_facet | Shahram Payandeh Jeffrey Wael |
| author_sort | Shahram Payandeh |
| collection | DOAJ |
| description | Tracking movements of the body in a natural living environment of a person is a challenging undertaking. Such tracking information can be used as a part of detecting any onsets of anomalies in movement patterns or as a part of a remote monitoring environment. The tracking information can be mapped and visualized using a virtual avatar model of the tracked person. This paper presents an initial novel experimental study of using a commercially available deep-learning body tracking system based on an RGB-D sensor for virtual human model reconstruction. We carried out our study in an indoor environment under natural conditions. To study the performance of the tracker, we experimentally study the output of the tracker which is in the form of a skeleton (stick-figure) data structure under several conditions in order to observe its robustness and identify its drawbacks. In addition, we show and study how the generic model can be mapped for virtual human model reconstruction. It was found that the deep-learning tracking approach using an RGB-D sensor is susceptible to various environmental factors which result in the absence and presence of noise in estimating the resulting locations of skeleton joints. This as a result introduces challenges for further virtual model reconstruction. We present an initial approach for compensating for such noise resulting in a better temporal variation of the joint coordinates in the captured skeleton data. We explored how the extracted joint position information of the skeleton data can be used as a part of the virtual human model reconstruction. |
| format | Article |
| id | doaj-art-2b36d202c7dc4288b73161ceea318f85 |
| institution | Kabale University |
| issn | 1687-6415 1687-6423 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Telemedicine and Applications |
| spelling | doaj-art-2b36d202c7dc4288b73161ceea318f852025-08-20T03:54:43ZengWileyInternational Journal of Telemedicine and Applications1687-64151687-64232021-01-01202110.1155/2021/55517535551753Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model ReconstructionShahram Payandeh0Jeffrey Wael1Networked Robotics and Sensing Laboratory, School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, CanadaNetworked Robotics and Sensing Laboratory, School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, CanadaTracking movements of the body in a natural living environment of a person is a challenging undertaking. Such tracking information can be used as a part of detecting any onsets of anomalies in movement patterns or as a part of a remote monitoring environment. The tracking information can be mapped and visualized using a virtual avatar model of the tracked person. This paper presents an initial novel experimental study of using a commercially available deep-learning body tracking system based on an RGB-D sensor for virtual human model reconstruction. We carried out our study in an indoor environment under natural conditions. To study the performance of the tracker, we experimentally study the output of the tracker which is in the form of a skeleton (stick-figure) data structure under several conditions in order to observe its robustness and identify its drawbacks. In addition, we show and study how the generic model can be mapped for virtual human model reconstruction. It was found that the deep-learning tracking approach using an RGB-D sensor is susceptible to various environmental factors which result in the absence and presence of noise in estimating the resulting locations of skeleton joints. This as a result introduces challenges for further virtual model reconstruction. We present an initial approach for compensating for such noise resulting in a better temporal variation of the joint coordinates in the captured skeleton data. We explored how the extracted joint position information of the skeleton data can be used as a part of the virtual human model reconstruction.http://dx.doi.org/10.1155/2021/5551753 |
| spellingShingle | Shahram Payandeh Jeffrey Wael Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction International Journal of Telemedicine and Applications |
| title | Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction |
| title_full | Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction |
| title_fullStr | Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction |
| title_full_unstemmed | Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction |
| title_short | Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction |
| title_sort | experimental study of a deep learning rgb d tracker for virtual remote human model reconstruction |
| url | http://dx.doi.org/10.1155/2021/5551753 |
| work_keys_str_mv | AT shahrampayandeh experimentalstudyofadeeplearningrgbdtrackerforvirtualremotehumanmodelreconstruction AT jeffreywael experimentalstudyofadeeplearningrgbdtrackerforvirtualremotehumanmodelreconstruction |