Regularized Landmark Detection with CAEs for Human Pose Estimation in the Operating Room

Lasse Hansen, Jasper Diesel, Mattias P. Heinrich

Abstract

Robust estimation of the human pose is a critical requirement for the development of context aware assistance and monitoring systems in clinical settings. Environments like operating rooms or intensive care units pose different visual challenges for the problem of human pose estimation such as frequent occlusions, clutter and difficult lighting conditions. Moreover, privacy concerns play a major role in health care applications and make it necessary to use unidentifiable data, e.g. blurred RGB images or depth frames. Since, for this reason, the data basis is much smaller than for human pose estimation in common scenarios, pose priors could be beneficial for regularization to train robust estimation models. In this work, we investigate to what extent existing pose estimation methods are suitable for the challenges of clinical environments and propose a CAE based regularization method to correct estimated poses that are anatomically implausible. We show that our models trained solely on depth images reach similar results on the MVOR dataset [1] as RGB based pose estimators while intrinsically being non-identifiable. In further experiments we prove that our CAE regularization can cope with several pose perturbations, e.g. missing parts or left-right flips of joints.

OriginalspracheEnglisch
TitelBildverarbeitung für die Medizin 2019
Seitenumfang6
Herausgeber (Verlag)Springer Verlag
Erscheinungsdatum01.01.2019
Seiten178-183
ISBN (Print)978-3-658-25325-7
ISBN (elektronisch)978-3-658-25326-4
DOIs
PublikationsstatusVeröffentlicht - 01.01.2019
VeranstaltungWorkshop on Bildverarbeitung fur die Medizin 2019 - Lübeck, Deutschland
Dauer: 17.03.201919.03.2019
Konferenznummer: 224899

Fingerprint

Untersuchen Sie die Forschungsthemen von „Regularized Landmark Detection with CAEs for Human Pose Estimation in the Operating Room“. Zusammen bilden sie einen einzigartigen Fingerprint.

Zitieren