MIDL 2021

International Conference on
Medical Imaging with Deep Learning
5. bis 7. Juli 2021

HRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints

TitleHRDepthNet: Depth Image-Based Marker-Less Tracking of Body Joints
Publication TypeJournal Article
Year of Publication2021
AuthorsBüker L.C., Zuber F., Hein A., Fudickar S.
Date Published2021
Publication Languageeng

With approaches for the detection of joint positions in color images such as HRNet and OpenPose being available, consideration of corresponding approaches for depth images is limited even though depth images have several advantages over color images like robustness to light variation or color- and texture invariance. Correspondingly, we introduce High- Resolution Depth Net (HRDepthNet)—a machine learning driven approach to detect human joints (body, head, and upper and lower extremities) in purely depth images. HRDepthNet retrains the original HRNet for depth images. Therefore, a dataset is created holding depth (and RGB) images recorded with subjects conducting the timed up and go test—an established geriatric assessment. The images were manually annotated RGB images. The training and evaluation were conducted with this dataset. For accuracy evaluation, detection of body joints was evaluated via COCO’s evaluation metrics and indicated that the resulting depth image-based model achieved better results than the HRNet trained and applied on corresponding RGB images. An additional evaluation of the position errors showed a median deviation of 1.619 cm (x-axis), 2.342 cm (y-axis) and 2.4 cm (z-axis).

Created at November 22, 2021 - 2:56pm by Fudickar.


Program of Study

Study Medical Informatics
at the University of Lübeck

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