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Computer Vision for Medical Infant Motion Analysis: State of the Art and RGB-D Data Set

: Hesse, Nikolas; Bodensteiner, Christoph; Arens, Michael; Hofmann, Ulrich G.; Weinberger, Raphael; Schroeder, Sebastian A.

Postprint urn:nbn:de:0011-n-5344201 (5.0 MByte PDF)
MD5 Fingerprint: 7834d4bdc61f4bbf3e49413448042f35
The original publication is available at
Erstellt am: 14.09.2019

Leal-Taixé, L.:
Computer Vision - ECCV Workshops 2018 : Munich, Germany, September 8-14, 2018, Proceedings, Part VI
Cham: Springer International Publishing, 2019 (Lecture Notes in Computer Science 11134)
ISBN: 978-3-030-11023-9 (Print)
ISBN: 978-3-030-11024-6 (Online)
European Conference on Computer Vision (ECCV) <15, 2018, Munich>
International Workshop on Assistive Computer Vision and Robotics <6, 2018, Munich>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Assessment of spontaneous movements of infants lets trained experts predict neurodevelopmental disorders like cerebral palsy at a very young age, allowing early intervention for affected infants. An automated motion analysis system requires to accurately capture body movements, ideally without markers or attached sensors to not affect the movements of infants. A vast majority of recent approaches for human pose estimation focuses on adults, leading to a degradation of accuracy if applied to infants. Hence, multiple systems for infant pose estimation have been developed. Due to the lack of publicly available benchmark data sets, a standardized evaluation, let alone a comparison of different approaches is impossible. We fill this gap by releasing the Moving INfants In RGB-D (MINI-RGBD) (Data set available for research purposes at data set, created using the recently introduced Skinned Multi-Infant Linear body model (SMIL). We map real infant movements to the SMIL model with realistic shapes and textures, and generate RGB and depth images with precise ground truth 2D and 3D joint positions. We evaluate our data set with state-of-the-art methods for 2D pose estimation in RGB images and for 3D pose estimation in depth images. Evaluation of 2D pose estimation results in a PCKh rate of 88.1% and 94.5% (depending on correctness threshold), and PCKh rates of 64.2%, respectively 90.4% for 3D pose estimation. We hope to foster research in medical infant motion analysis to get closer to an automated system for early detection of neurodevelopmental disorders.