In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image
Domains
- URL: http://arxiv.org/abs/2111.15124v1
- Date: Tue, 30 Nov 2021 04:56:16 GMT
- Title: In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image
Domains
- Authors: Ting Cao, Mohammad Ali Armin, Simon Denman, Lars Petersson, David
Ahmedt-Aristizabal
- Abstract summary: In-bed human posture estimation provides important health-related metrics with potential value in medical condition assessments.
We propose a multi-modal conditional variational autoencoder (MC-VAE) capable of reconstructing features from missing modalities seen during training.
We demonstrate that body positions can be effectively recognized from the available modality, achieving on par results with baseline models.
- Score: 22.92165116962952
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Medical applications have benefited from the rapid advancement in computer
vision. For patient monitoring in particular, in-bed human posture estimation
provides important health-related metrics with potential value in medical
condition assessments. Despite great progress in this domain, it remains a
challenging task due to substantial ambiguity during occlusions, and the lack
of large corpora of manually labeled data for model training, particularly with
domains such as thermal infrared imaging which are privacy-preserving, and thus
of great interest. Motivated by the effectiveness of self-supervised methods in
learning features directly from data, we propose a multi-modal conditional
variational autoencoder (MC-VAE) capable of reconstructing features from
missing modalities seen during training. This approach is used with HRNet to
enable single modality inference for in-bed pose estimation. Through extensive
evaluations, we demonstrate that body positions can be effectively recognized
from the available modality, achieving on par results with baseline models that
are highly dependent on having access to multiple modes at inference time. The
proposed framework supports future research towards self-supervised learning
that generates a robust model from a single source, and expects it to
generalize over many unknown distributions in clinical environments.
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