TY - JOUR
T1 - Non-Contact Human Body Measurement Based on 2D Imagery
AU - Cai , Jin-Yuan
AU - Wu , Tian-Tian
AU - Xiao , Ai-Min
JO - Journal of Fiber Bioengineering and Informatics
VL - 3
SP - 179
EP - 189
PY - 2024
DA - 2024/11
SN - 17
DO - http://doi.org/10.3993/jfbim02971
UR - https://global-sci.org/intro/article_detail/jfbi/23563.html
KW - 2D Picture, Deep Learning, Image Segmentation, Human Body Shape Studies.
AB - <p style="text-align: justify;">Analysing human body shapes requires a substantial amount of anthropometric data. However,
traditional manual measurement methods are often inefficient, while 3D scanning devices are expensive
and inconvenient. To address these challenges, this study presents a method based on the U2-Net neural
network model for extracting human body contours in complex backgrounds, feature point extraction,
and circumference fitting analysis. Using data enhancement techniques, we utilized a dataset comprising
2 560 frontal and lateral images of individuals against various backgrounds, which was augmented to
42 800 images. Subsequently, a deep learning network model was trained to accurately fit chest, waist,
and hip circumference measurements. Finally, 20 samples were selected for validation, and the predicted
values were compared with manually measured values, showing that the errors fall within an acceptable
range. The effectiveness and accuracy of this method have been validated, providing a practical solution
for anthropometric data collection and body shape research in remote areas.</p>