Automatic Estimation of Anthropometric Human Body Measurements

Škorvánková, Dana; Riečický, Adam; Madaras, Martin

doi:10.48550/arxiv.2112.11992

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Second, the comparison results on the synthetic dataset are shown in Table 2. The comparison results show that the proposed method provided better measurement performance than other state-of-the-art methods, such as HS-Net [14], HKS-Net [3], and BoDiEs [20]. In addition, we evaluated our method on NOMO-3d-400-scans Dataset and the real image data from 10 volunteers.…”

Section: Resultsmentioning

confidence: 98%

Estimation Of 3d Body Shape And Clothing Measurements From Frontal-And Side-View Images

Thota

Suh

Zhang

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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The estimation of 3D human body shape and clothing measurements is crucial for virtual try-on and size recommendation problems in the fashion industry but has always been a challenging problem due to several conditions, such as lack of publicly available realistic datasets, ambiguity in multiple camera resolutions, and the undefinable human shape space. Existing works proposed various solutions to these problems but could not succeed in the industry adaptation because of complexity and restrictions. To solve the complexity and challenges, in this paper, we propose a simple yet effective architecture to estimate both shape and measures from frontal-and side-view images. We utilize silhouette segmentation from the two multi-view images and implement an auto-encoder network to learn low-dimensional features from segmented silhouettes. Then, we adopt a kernel-based regularized regression module to estimate the body shape and measurements. The experimental results show that the proposed method provides competitive results on the synthetic dataset, NOMO-3d-400-scans Dataset, and RGB Images of humans captured in different cameras.

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Section: Resultsmentioning

confidence: 98%

Estimation Of 3d Body Shape And Clothing Measurements From Frontal-And Side-View Images

Thota

Suh

Zhang

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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“…Liu et al [ 13 ] stated that 3D scanners have fundamentally changed the approach to this type of anthropometric measurement in recent years. In [ 14 ], a synthetic data set of human body shapes was used to develop a method for estimating anthropometric parameters using deep learning and neural networks.…”

Section: Introductionmentioning

confidence: 99%

System for Estimation of Human Anthropometric Parameters Based on Data from Kinect v2 Depth Camera

Krzeszowski

Dziadek

França

et al. 2023

Sensors

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Anthropometric measurements of the human body are an important problem that affects many aspects of human life. However, anthropometric measurement often requires the application of an appropriate measurement procedure and the use of specialized, sometimes expensive measurement tools. Sometimes the measurement procedure is complicated, time-consuming, and requires properly trained personnel. This study aimed to develop a system for estimating human anthropometric parameters based on a three-dimensional scan of the complete body made with an inexpensive depth camera in the form of the Kinect v2 sensor. The research included 129 men aged 18 to 28. The developed system consists of a rotating platform, a depth sensor (Kinect v2), and a PC computer that was used to record 3D data, and to estimate individual anthropometric parameters. Experimental studies have shown that the precision of the proposed system for a significant part of the parameters is satisfactory. The largest error was found in the waist circumference parameter. The results obtained confirm that this method can be used in anthropometric measurements.

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“…Anthropometric data are the basic data of national production and development, which play an important role in costume design, health assessment and industrial design to guarantee a healthy and comfortable user experience [ 1 , 2 , 3 , 4 ]. Manual anthropometric measurement mainly depends on the experience of the surveyor, whose accuracy fluctuates with different surveyors and whose efficiency is restricted by the surveyor [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System

Yang

Guo

Song

et al. 2022

Entropy

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This paper proposes an improved human-body-segmentation algorithm with attention-based feature fusion and a refined corner-based feature-point design with sub-pixel stereo matching for the anthropometric system. In the human-body-segmentation algorithm, four CBAMs are embedded in the four middle convolution layers of the backbone network (ResNet101) of PSPNet to achieve better feature fusion in space and channels, so as to improve accuracy. The common convolution in the residual blocks of ResNet101 is substituted by group convolution to reduce model parameters and computational cost, thereby optimizing efficiency. For the stereo-matching scheme, a corner-based feature point is designed to obtain the feature-point coordinates at sub-pixel level, so that precision is refined. A regional constraint is applied according to the characteristic of the checkerboard corner points, thereby reducing complexity. Experimental results demonstrated that the anthropometric system with the proposed CBAM-based human-body-segmentation algorithm and corner-based stereo-matching scheme can significantly outperform the state-of-the-art system in accuracy. It can also meet the national standards GB/T 2664-2017, GA 258-2009 and GB/T 2665-2017; and the textile industry standards FZ/T 73029-2019, FZ/T 73017-2014, FZ/T 73059-2017 and FZ/T 73022-2019.

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Automatic Estimation of Anthropometric Human Body Measurements

Cited by 3 publications

References 15 publications

Estimation Of 3d Body Shape And Clothing Measurements From Frontal-And Side-View Images

Estimation Of 3d Body Shape And Clothing Measurements From Frontal-And Side-View Images

System for Estimation of Human Anthropometric Parameters Based on Data from Kinect v2 Depth Camera

An Improved Human-Body-Segmentation Algorithm with Attention-Based Feature Fusion and a Refined Stereo-Matching Scheme Working at the Sub-Pixel Level for the Anthropometric System

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