Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (01): 120-129.doi: 10.13475/j.fzxb.20180306210

• Apparel Engineering • Previous Articles     Next Articles

Automatic measurement of three-dimensional human body based on scattered point cloud

BAO Chen1, MIAO Yongwei2(), SUN Yuliang1, ZHANG Xudong1   

  1. 1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
    2. School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2018-03-26 Revised:2018-10-03 Online:2019-01-15 Published:2019-01-18
  • Contact: MIAO Yongwei E-mail:ywmiao@zstu.edu.cn

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Abstract:

In order to solve the human body's characteristic size extraction in 3-D human body scanning reconstruction using Kinect depth camera, an automatic measurement scheme of 3-D human body size based on scattered point cloud was proposed. Firstly, the collected 3-D point cloud was preprocessed, and then the front and back surfaces of a human body were identified by point cloud desampling, outlier filtering, surface reconstruction and point cloud coordinate transformation. Then, the human body geometric shape analysis method was used to automatically extract human body's characteristic points and characteristic section point cloud. Finally, the characteristic boundary points were extracted from the obtained characteristic cross section point cloud, and then the characteristic boundary line was obtained by the contour extraction algorithm based on the convex hull, the girth and arc length were calculated through the cubic B-spline curve fittings, and the length was calculated by coordinate difference and Euclidean distance, so that the characteristic size measurement of the 3-D human body point cloud model was completed. The experiment results show that the scheme is less affected by human body shape factors, and the proposed automatic extraction scheme of size information is effective. The feasibility is verified according to the measurement accuracy requirements of related standard.

Key words: scattered point cloud, point cloud segmentation, characteristic point of human body, 3-D human body measurement

CLC Number: 

  • TS941.26

Fig.1

Measurement system device of human body sizes"

Fig.2

Flow chart of human body size measurement"

Fig.3

Cloud data preprocessing of human body points."

Tab.1

Proportion of position of human body characteristic points and body height"

特征点名称 特征点位置与身高的比例
男 女
头顶点 1.00 1.00
颈侧点 0.84 0.86
肩峰点 0.82 0.81
腋窝点 0.75 0.75
胸高点 0.72 0.72
腰侧点 0.61 0.63
臀凸点 0.53 0.53
会阴点 0.47 0.47
膝盖点 0.26 0.28
外踝点 0.05 0.05
脚点 0.00 0.00

Fig.4

Segmentation results of human body point cloud."

Fig.5

Neck base line extraction."

Fig.6

Judging boundary feature points."

Fig.7

B-spline fitting of chest circumference convex-hull."

Fig.8

B-spline fitting of waist circumference convex-hull."

Fig.9

B-spline fitting of back breadth convex-hull."

Fig.10

Interactive measurement interface"

Tab.2

Precision analysis of measurement results cm"

测量
项目		 观察者(i) 均值 IQR 标准差 CV值/%
第1次 第2次 第3次 第4次 第5次
身高 169.06 168.97 169.17 169.42 168.82 169.09 0.20 0.225 544 0.13
胸围 87.83 87.96 88.14 87.72 88.32 87.99 0.31 0.240 167 0.27
腰围 79.02 79.14 79.17 78.80 79.46 79.12 0.15 0.240 250 0.30
臀围 93.09 93.16 93.26 92.43 92.81 92.95 0.35 0.335 336 0.36
肩宽 44.79 44.57 44.12 43.76 43.33 44.11 0.81 0.592 647 1.34
臂长 59.12 59.30 59.48 59.51 58.61 59.20 0.36 0.367 056 0.62
上身长 69.86 69.62 69.98 69.57 69.63 69.73 0.24 0.178 241 0.26
腿内侧长 75.12 75.06 74.77 74.92 74.86 74.95 0.20 0.143 457 0.19

Fig.11

Measurement items of human body dimessions"

Tab.3

Error analysis of measurement cm"

测量
项目		 本文方案
最大误差		 本文方案
最小误差		 本文方案
平均误差		 文献[4]
最大误差		 文献[18]
最大误差
身高 0.36 0.15 0.08 1.7
胸围 0.80 0.11 0.42 1.6 1.88
腰围 0.75 0.24 0.47 2.5 1.25
臀围 0.67 0.09 0.39 0.7 1.65
肩宽 0.85 0.31 0.78 1.6
臂长 0.38 0.1 0.21

Fig.12

Measurement error boxplot"

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