Parsing human skeletons in an operating room

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

    Research output: Contribution to journalArticle

    16 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)1035-1046
    Number of pages12
    JournalMachine Vision and Applications
    Issue number7
    Publication statusPublished - 2016 Oct 1


    • Human pose estimation
    • Medical workflow analysis
    • Part-based model

    ASJC Scopus subject areas

    • Software
    • Hardware and Architecture
    • Computer Vision and Pattern Recognition
    • Computer Science Applications

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  • Cite this

    Belagiannis, V., Wang, X., Shitrit, H. B. B., Hashimoto, K., Stauder, R., Aoki, Y., Kranzfelder, M., Schneider, A., Fua, P., Ilic, S., Feussner, H., & Navab, N. (2016). Parsing human skeletons in an operating room. Machine Vision and Applications, 27(7), 1035-1046.