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

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.