BlanketSet -- A clinical real-world in-bed action recognition and qualitative semi-synchronised MoCap dataset
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Abstract (arXiv)
Clinical in-bed video-based human motion analysis is a very relevant computer vision topic for several relevant biomedical applications. Nevertheless, the main public large datasets (e.g. ImageNet or 3DPW) used for deep learning approaches lack annotated examples for these clinical scenarios. To address this issue, we introduce BlanketSet, an RGB-IR-D action recognition dataset of sequences performed in a hospital bed. This dataset has the potential to help bridge the improvements attained in more general large datasets to these clinical scenarios. Information on how to access the dataset is available at https://rdm.inesctec.pt/dataset/nis-2022-004.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 7 Oct 2022.
Builds on
5 of 17 references.
References · 5
- 2017The Kinetics Human Action Video Datasetcited by 1,894
- 2016Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervisioncited by 493
- 2020HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimationcited by 304
- 2022DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimationcited by 29
- 2021Out-of-Domain Human Mesh Reconstruction via Dynamic Bilevel Online Adaptationcited by 24
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.