Non-Contact Human Body Measurement Based on 2D Imagery (Journal of Fiber Bioengineering and Informatics, 2024)
Analysing human body shapes requires a substantial amount of anthropometric data. However,traditional manual measurement methods are often inefficient, while 3D scanning devices are expensiveand inconvenient. To address these challenges, this study presents a method based on the U2-Net neuralnetwork model for extracting human body contours in complex backgrounds, feature point extraction,and circumference fitting analysis. Using data enhancement techniques, we utilized a dataset comprising2560 frontal and lateral images of individuals against various backgrounds, which was augmented to42800 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 predictedvalues were compared with manually measured values, showing that the errors fall within an acceptablerange. The effectiveness and accuracy of this method have been validated, providing a practical solutionfor anthropometric data collection and body shape research in remote areas.