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Non-Contact Human Body Dimension Estimation Methods Based on Deep Learning: A Critical Analysis
- 1 School of Artificial Intelligence, South China Normal University, Guangzhou, China
* Author to whom correspondence should be addressed.
Abstract
With the advancement of information technology, Human Body Dimension Estimation (HBDE) is moving towards digitalization and non-contact methods. This paper critically reviews non-contact HBDE methods based on deep learning, primarily covering image-based methods, the Adaptive Body Structure Segmentation (ABSS) algorithm with height and weight measurements, and 3D meshes or point cloud data methods. Convolutional Neural Networks (CNNs) excel in image measurement, while the ABSS algorithm with adaptive segmentation achieves high accuracy. Recent studies, such as the Grey Wolf Optimizer-Elman Neural Network (GBWO-ENN) model, have significantly improved prediction accuracy by optimizing neural network structures. Additionally, 3D point cloud data combined with the Skinned Multi-Person Linear (SMPL) model has proven effective in complex scenarios. This paper summarizes the performance of various methods, analyzes common datasets, and explores future development directions for non-contact HBDE. Despite significant progress, this field still faces challenges such as data quality, scarcity of extreme samples, and privacy issues. Future research should focus on building high-quality datasets and developing lightweight measurement networks to meet practical application needs.
Keywords
Non-contact measurement, human body dimension estimation, deep learning, convolutional neural networks
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Cite this article
Zhang,Y. (2024). Non-Contact Human Body Dimension Estimation Methods Based on Deep Learning: A Critical Analysis. Theoretical and Natural Science,52,214-221.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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