Human featured automatic measurement system

Xu Huiming; Xia Likun; Yang Qi; Liu Yujie; Tang Songyuan; Yang Jian

doi:10.11834/jig.20170210

Views : 0 下载量: 1831 CSCD: 4

PDF
Export
Share
Collection
Album

Human featured automatic measurement system
Vol. 22, Issue 2, Pages: 225-231(2017)
Published Online：21 January 2017，

Published：2017
DOI： 10.11834/jig.20170210
稿件说明：

移动端阅览

DOI：

Xu Huiming, Xia Likun, Yang Qi, Liu Yujie, Tang Songyuan, Yang Jian. Human featured automatic measurement system[J]. Journal of Image and Graphics, 2017, 22(2): 225-231. DOI： 10.11834/jig.20170210.

摘要

针对国内外已有的非接触式测量系统普遍存在价格昂贵、安装复杂、占用空间大等问题，提出一种基于消失点和图像比例法相结合的人体参数尺寸的自动测量方法(HuFAMS-VP)。首先，利用背景差分法，将人体从图像中分割出来；再将分割后的人体图像进行轮廓提取，从而获得人体轮廓图；然后，根据边缘检测和人体各个部位与人体身高比例等方法，获取人体几个关键部位特征点；最后，通过消失点方法(vanishing point)与比例法的结合获取人体参数尺寸。根据HuFAMS-VP对7组被测人员(年龄在25±3岁，身高范围在1.7±0.2 m)进行尺寸测量，并与被测人员真实尺寸信息进行比较。由测量结果可以得到7组被测人员的绝对误差控制在±0.05 m之内，平均测量速度在2 s以内。 HuFAMS-VP在确保测量精度的前提下，具有操作方便、价格低廉等优势。根据测量结果，论证了本系统的准确性和鲁棒性。

Abstract

The common obstacles faced by all types of automatic human body measurement systems are comparably high costs and large sizes with a complex installation process. This study proposes a human-featured automatic measurement system based on vanishing points (HuFAMS-VP). It consists of the following parts. HuFAMS-VP initially extracts a human body image

utilizing the background subtraction method followed by image contour detection. Several critical characteristic points on the contour can then be obtained by using the edge detection technique and proportion method. Finally

we combine the methods of vanishing points with proportion measure to obtain different human body features. HuFAM-VP is applied to measure the human body size

and the measurements are compared with the true sizes. Results show that it can achieve accurate results at less than±5 cm between the measured and real data. Moreover

the average CPU time is less than 2 s. The proposed system has the advantages of easy operation and low cost under the premise of high accuracy. Results show that HuFAM-VP demonstrated validity and robustness.