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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 7/2016

01.10.2016 | Original Paper

Parsing human skeletons in an operating room

verfasst von: Vasileios Belagiannis, Xinchao Wang, Horesh Beny Ben Shitrit, Kiyoshi Hashimoto, Ralf Stauder, Yoshimitsu Aoki, Michael Kranzfelder, Armin Schneider, Pascal Fua, Slobodan Ilic, Hubertus Feussner, Nassir Navab

Erschienen in: Machine Vision and Applications | Ausgabe 7/2016

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Abstract

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.
Vorheriger Artikel Encouraging second-order consistency for multiple graph matching
Nächster Artikel Motion estimation using learning automata

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Metadaten
Titel
Parsing human skeletons in an operating room
verfasst von
Vasileios Belagiannis
Xinchao Wang
Horesh Beny Ben Shitrit
Kiyoshi Hashimoto
Ralf Stauder
Yoshimitsu Aoki
Michael Kranzfelder
Armin Schneider
Pascal Fua
Slobodan Ilic
Hubertus Feussner
Nassir Navab
Publikationsdatum
01.10.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 7/2016
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-016-0792-4

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