Parsing human skeletons in an operating room |
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| Authors: | 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 |
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| Affiliation: | 1.Computer Aided Medical Procedures,Technische Universit?t München,Munich,Germany;2.VGG,University of Oxford,Oxford,UK;3.CVLAB,Ecole Polytechnique Fédérale de Lausanne (EPFL),Lausanne,Switzerland;4.Aoki Media Sensing Lab,Keio University,Tokyo,Japan;5.MITI, Klinikum rechts der Isar,Technische Universit?t München,Munich,Germany;6.Siemens AG,Munich,Germany;7.Johns Hopkins University,Baltimore,USA |
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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. |
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