纺织学报 ›› 2023, Vol. 44 ›› Issue (09): 175-179.doi: 10.13475/j.fzxb.20220707001

• 服装工程 • 上一篇    下一篇

基于图像的人体肩部特征点检测方法

邓中民1, 王健恺1, 靳晓凝1, 魏宛彤2, 于东洋1, 柯薇1()   

  1. 1.武汉纺织大学 省部共建纺织新材料与先进加工技术国家重点实验室, 湖北 武汉 430200
    2.武汉纺织大学 服装学院, 湖北 武汉 430200
  • 收稿日期:2022-07-20 修回日期:2023-01-10 出版日期:2023-09-15 发布日期:2023-10-30
  • 通讯作者: 柯薇(1986—),女,副教授,博士。主要研究方向为纺织品的数字化设计与开发。E-mail:wke@wtu.edu.cn
  • 作者简介:邓中民(1963—),男,教授,博士。主要研究方向为数字化纺织。
  • 基金资助:
    湖北省技术创新专项项目(2019AAA005)

Detection of human shoulder feature points based on image analysis

DENG Zhongmin1, WANG Jiankai1, JIN Xiaoning1, WEI Wantong2, YU Dongyang1, KE Wei1()   

  1. 1. State Key Laboratory of New Textile Materials and Advanced Processing Technology, Wuhan Textile University, Wuhan, Hubei 430200, China
    2. School of Fashion, Wuhan Textile University, Wuhan, Hubei 430200, China
  • Received:2022-07-20 Revised:2023-01-10 Published:2023-09-15 Online:2023-10-30

摘要:

针对现有检测算法对肩部特征点的误检、漏检和检测误差大等问题,提出了一种改进的基于人体轮廓编码的肩部特征点的检测方法。该方法以人体所占图像的大小来确定特征链码串的长度,并且通过特征区域分割、特征区域遍历,具有几何特征的链码串动态筛选的搜索方式,筛选出符合条件的肩部特征点。在获取肩部特征点以后,计算出肩宽值,并且与实际人工测量的肩宽值进行比对与分析。本文实验共选取了100名不同身型的青年测试者(50男/50女)在同一拍摄环境下对该方法的可行性进行测试。结果表明,采用该方法能够快速识别人体的肩部特征点,与人工测量值对比得出的平均误差仅在3%左右,较好地提升了二维人体肩宽尺寸检测的效率,并且为后续服装匹配等相关工作提供了更加精确的数据支持。

关键词: 肩部特征点, 肩峰点, 人体轮廓编码, 人体尺寸测量, 特征链码串, 肩宽

Abstract:

Objective Human body size data plays an important reference role in measuring human body type. Along with the continuous development of computer technology, non-contact anthropocentric methods based on two-dimensional images were reported to obtain human body size data, the accuracy of which depends largely on accuracy of the feature points of each part detected using these methods. For the shoulder, which is a feature part with fewer obvious feature point characteristics, the existing two-dimensional detection methods leads to large errors in the feature points, calling for further improvement.

Method In order to address the problems of false detection, missed detection, and large detection errors of the existing detection algorithms for shoulder feature points, an improved detection method for shoulder feature points based on human contour coding was proposed. The method was capable of determining the length of the feature chain code string by the size of the image occupied by the human body and selecting the feature points of the shoulder by a search method of feature partitioning, feature region traversal, and dynamic screening of the chain code string with geometric features. After acquiring the shoulder feature points, the shoulder width values were calculated based on the location of the feature points and compared with the actual manually measured shoulder width values to analyze the experimental results.

Results In this research, a total of 100 young testers of different body types (50 males/50 females) were selected to test the feasibility of the method under the same shooting environment. The experimental study showed that the method can quickly and accurately identify the shoulder feature points of the human body, and the locations of the selected feature points exist and are single, reducing the occurrence of false detection and omission (Tab. 1). The average error derived was only about 3%, which verifies the feasibility of the method.

Conclusion While maintaining the advantage of the low cost of the 2-D measurement method, it also greatly improves the efficiency of 2-D human shoulder width size detection, enabling it to provide more accurate data support in the subsequent human body shape research, clothing matching and professionalization, and other related fields.

Key words: shoulder feature point, acromial point, human contour coding, body size measurement, feature chain code string, shoulder width

中图分类号: 

  • TS941.17

图1

高斯滤波平滑处理前后效果对比图"

图2

单通道提取效果图"

图3

阈值分割效果图"

图4

人体轮廓图"

图5

像素角"

图6

遍历优先级设置"

图7

肩部特征点检测效果图"

表1

肩部特征点检测"

编号 左肩
x1
左肩
y1
右肩
x2
右肩
y2
测量肩
宽/cm
实际肩
宽/cm
误差/
%
1 387 330 662 340 39.5 40.2 1.7
2 320 327 612 319 38.8 38.1 1.8
3 238 333 712 335 44.7 46.2 3.2
4 317 358 632 328 39.7 38.6 2.8
5 337 325 610 308 35.5 36.2 1.9
6 287 372 562 365 37.5 36.3 3.3
7 222 293 702 304 44.1 43.1 2.3
8 207 341 652 355 42.6 41.5 2.6
9 192 358 714 361 43.1 41.7 3.3
10 357 561 730 564 41.5 41.6 0.2
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