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Body size measurement based on deep learning for image segmentation by binocular stereovision system

  • 1221: Deep Learning for Image/Video Compression and Visual Quality Assessment
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Abstract

This paper proposes a body size measurement system based on deep learning for image segmentation by binocular stereovision for clothing design. A pair of binocular stereo cameras and a precise revolving platform are used to collect the stereo images of the entire human body. A more diverse human body dataset is constructed for deep learning. An optimized girth semantic segmentation model based on PSPNet network is well trained by deep learning to realize the automatic segmentation of more girth regions in human body images taken from multiple angles. Color subspaces with larger spacing in color spaces are designed to perform more effective stereo matching. The space coordinates of 3D point corresponding to each stereo matching point pair are calculated in the same coordinate system. The space coordinates are reversely revolved back to the intial coordinates of the same coordinate system according to the revolving angle of the platform. Girths of bust, waist, hip and thigh are measured by calculating the fitting curve length after girth fitting of the markers thereon. Height of bust, waist, hip, thigh and entire body are estimated according to the ratio of the 3D distance to the number of pixels between two adjacent markers on the same girth. Quantitative experiments on many human subjects demonstrate that the proposed system can measure body size intelligently and accurately for clothing design under the regulation of China national standards GB/T 2664-2017, GA258-2009, GB/T 2665-2017 and textile industry standard FZ/T 73029-2019, FZ/T 73017-2014, FZ/ T 73059-2017, FZ/T 73022-2019.

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Acknowledgements

This work was supported in part by the ZhongYuan Science and Technology Innovation Leading Talent Program under Grant 214200510013, in part by the Key Research Project of Colleges and Universities in Henan Province under Grant 19A510005, Grant 21A510016, and Grant 21A520052, in part by the Scientific Research Grants and Start-up Projects for Overseas Student under Grant HRSS2021[36], and in part by the Major Project Achievement Cultivation Plan of Zhongyuan University of Technology under Grant K2020ZDPY02.

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Authors and Affiliations

  1. School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou, China

    Xiaowei Song, Xianli Song & Lei Yang

  2. Kaifeng University, Kaifeng, China

    Xiaowei Song

  3. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Menglong Li & Chunping Hou

  4. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA

    Zixiang Xiong

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  1. Xiaowei Song
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Correspondence to Lei Yang.

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Song, X., Song, X., Yang, L. et al. Body size measurement based on deep learning for image segmentation by binocular stereovision system. Multimed Tools Appl 81, 42547–42572 (2022). https://doi.org/10.1007/s11042-021-11470-2

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