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Found 7 papers, 6 papers with code
Articulated Clinician Detection Using 3D Pictorial Structures on RGB-D Data
Proposed methods for the operating room (OR) rely either on foreground estimation using a multi-camera system, which is a challenge in real ORs due to color similarities and frequent illumination changes, or on wearable sensors or markers, which are invasive and therefore difficult to introduce in the room.
A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms
In this paper, we propose an approach for multi-view 3D human pose estimation from RGB-D images and demonstrate the benefits of using the additional depth channel for pose refinement beyond its use for the generation of improved features.
MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation
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.
Face Detection in the Operating Room: Comparison of State-of-the-art Methods and a Self-supervised Approach
Methods: We propose a comparison of 6 state-of-the-art face detectors on clinical data using Multi-View Operating Room Faces (MVOR-Faces), a dataset of operating room images capturing real surgical activities.
Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images
Human pose estimation (HPE) is a key building block for developing AI-based context-aware systems inside the operating room (OR).
Self-supervision on Unlabelled OR Data for Multi-person 2D/3D Human Pose Estimation
2D/3D human pose estimation is needed to develop novel intelligent tools for the operating room that can analyze and support the clinical activities.
Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room
Second, to address the domain shift and the lack of annotations, we propose a novel unsupervised domain adaptation method, called AdaptOR, to adapt a model from an in-the-wild labeled source domain to a statistically different unlabeled target domain.
