Modeling Human Motion for Predicting Usage of Hospital Operating Room

Ilyes Sghir, Shishir Shah

Abstract

In this paper, we present a system that exploits existing video streams from a hospital operating room (OR) to infer OR usage states. We define OR states that are relevant for assessing OR usage efficiency. We adopt a holistic approach that involves the combination of two meaningful human motion features: gestures or upper body movements computed using optical flow and whole body movements computed through motion trajectories. The two features are independently modeled for each of the defined OR usage states and eventually fused to obtain a final decision. Our approach is tested on a large collection of videos and the results show that the combination of both human motion features provide significant discriminative power in understanding usage of an OR.

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Paper Citation


in Harvard Style

Sghir I. and Shah S. (2016). Modeling Human Motion for Predicting Usage of Hospital Operating Room . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 328-335. DOI: 10.5220/0005678003280335


in Bibtex Style

@conference{visapp16,
author={Ilyes Sghir and Shishir Shah},
title={Modeling Human Motion for Predicting Usage of Hospital Operating Room},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={328-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005678003280335},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Modeling Human Motion for Predicting Usage of Hospital Operating Room
SN - 978-989-758-175-5
AU - Sghir I.
AU - Shah S.
PY - 2016
SP - 328
EP - 335
DO - 10.5220/0005678003280335