Abstract
Human body volume (BV) is a useful biometric
feature for human identification and an important medical
indicator for monitoring body health. Traditional BV estimation
techniques such as underwater weighing and air displacement
demand a lot of equipment and are difficult to be performed
under some circumstances, for example, in clinical environments
when dealing with bedridden patients. In this contribution,
a novel vision-based method dubbed Point2PartVolume based
on deep learning is proposed to rapidly and accurately predict
the part-aware BVs from a single-depth image of the dressed
body. First, a novel multitask neural network is proposed for
jointly completing the partial body point clouds, predicting
the body shape under clothing and semantically segmenting
the reconstructed body into parts. Next, the estimated body
segments are fed into the proposed volume regression network
to estimate the partial volumes. A simple yet efficient twostep training strategy is proposed for improving the accuracy
of volume prediction regressed from point clouds. Compared
to existing methods, the proposed method addresses several
major challenges in vision-based human BV estimation, including
shape completion, pose estimation, body shape estimation under
clothing, body segmentation, and volume regression from point
clouds. Experimental results on both the synthetic data and
public real-world data show that our method achieved average
90% volume prediction accuracy and outperformed the relevant
state-of-the-art.
feature for human identification and an important medical
indicator for monitoring body health. Traditional BV estimation
techniques such as underwater weighing and air displacement
demand a lot of equipment and are difficult to be performed
under some circumstances, for example, in clinical environments
when dealing with bedridden patients. In this contribution,
a novel vision-based method dubbed Point2PartVolume based
on deep learning is proposed to rapidly and accurately predict
the part-aware BVs from a single-depth image of the dressed
body. First, a novel multitask neural network is proposed for
jointly completing the partial body point clouds, predicting
the body shape under clothing and semantically segmenting
the reconstructed body into parts. Next, the estimated body
segments are fed into the proposed volume regression network
to estimate the partial volumes. A simple yet efficient twostep training strategy is proposed for improving the accuracy
of volume prediction regressed from point clouds. Compared
to existing methods, the proposed method addresses several
major challenges in vision-based human BV estimation, including
shape completion, pose estimation, body shape estimation under
clothing, body segmentation, and volume regression from point
clouds. Experimental results on both the synthetic data and
public real-world data show that our method achieved average
90% volume prediction accuracy and outperformed the relevant
state-of-the-art.
Original language | English |
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Article number | 5502812 |
Journal | IEEE Transactions on Instrumentation and measurement |
Volume | 72 |
DOIs | |
Publication status | Published - 9 Jun 2023 |
Bibliographical note
Publisher Copyright:© 1963-2012 IEEE.