  Person detection and pose estimation is a key requirement to develop
intelligent context-aware assistance systems. To foster the development of
human pose estimation methods and their applications in the Operating Room
(OR), we release the Multi-View Operating Room (MVOR) dataset, the first public
dataset recorded during real clinical interventions. It consists of 732
synchronized multi-view frames recorded by three RGB-D cameras in a hybrid OR.
It also includes the visual challenges present in such environments, such as
occlusions and clutter. We provide camera calibration parameters, color and
depth frames, human bounding boxes, and 2D/3D pose annotations. In this paper,
we present the dataset, its annotations, as well as baseline results from
several recent person detection and 2D/3D pose estimation methods. Since we
need to blur some parts of the images to hide identity and nudity in the
released dataset, we also present a comparative study of how the baselines have
been impacted by the blurring. Results show a large margin for improvement and
suggest that the MVOR dataset can be useful to compare the performance of the
different methods.
