MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation
This dataset addresses the problem of developing context-aware assistance systems in healthcare by providing a resource for researchers, though it is incremental as it focuses on data collection rather than novel methods.
The authors tackled the lack of public datasets for human pose estimation in operating rooms by releasing the MVOR dataset, which includes 732 synchronized multi-view frames from three RGB-D cameras with 2D/3D pose annotations, and baseline results show a large margin for improvement.
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.