Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (7): 2099-2108.doi: 10.13229/j.cnki.jdxbgxb.20210981

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Method for 3D motion parameter measurement based on pose estimation

Lian-ming WANG1(),Xin WU2   

  1. 1.School of Marine Information Engineering，Hainan Tropical Ocean University，Sanya 572022，China
    2.School of Physics，Northeast Normal University，Changchun 130024，China
  • Received:2021-09-28 Online:2023-07-01 Published:2023-07-20

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Abstract:

Aiming at the problems of high measurement cost， complex data fusion， and few extracted features in traditional 3D motion parameter measurement methods， a non-contact measurement method of 3D motion parameters based on pose estimation is proposed. First of all， the use of high frame rate synchronous acquisition equipment to capture object motion video from different perspectives makes up for the shortcomings of blind spots that are prone to single-view capture targets. Secondly， using the 2D pose estimation model of the training target based on the deep transfer learning 2D pose estimation framework DeepLabCut， and using the model to estimate the 2D pose of the object from different perspectives， makes up for the shortcomings of the traditional vision method with fewer features. Third， the 2D pose estimation of objects in different directions is merged into a 3D pose estimation based on the principle of triangulation. Finally， according to the measurement requirements， the 3D motion parameters such as the displacement， speed， pose angle， frequency of the specified key points of the object can be calculated using the results of the 3D pose estimation. Taking the motion measurement of the planetary pendulum and the human hand as an example， we measured the 3D motion parameters of the two and analyzed the sources of error and the advantages and disadvantages of this paper. The experimental results show that the system measurement is accurate， and the visualization results correctly reflect the movement of the experimental subjects. This method provides a new idea for the non-contact measurement of 3D motion parameters of objects.

Key words: computer application, motion measurement, deep transfer learning, pose estimation, triangulation

CLC Number: 

  • TP183

Fig.1

Structure of measurement system and data processing flow"

Fig.2

Illustration of three pose angles determinedby any three key points"

Table 1

Device parameters"

相机型号OSG130?210UC
像素尺寸4.8?μm×4.8?μm
颜色空间RGB
最高分辨率1280×1024
初始帧率210 帧/s
触发方式软触发或者硬触发
数据接口USB3.0
计算机型号Dell
CPUInter@Xeon SP@6248R@3GHz
RAM128.0 GB
GPURTX3090
系统类型64?bit 操作系统 X64?based
操作系统Windows 10

Fig.3

Examples of planetary pendulum labeling and3D pose estimation"

Fig.4

Visualization of 3D motion trajectory of a key point of planetary pendulum"

Fig.5

Visualization of 2D motion trajectory of a keypoint of planetary pendulum"

Fig.6

Visualization of motion parameters of a keypoint of planetary pendulum"

Fig.7

Examples of hand annotation and 3D poseestimation"

Fig.8

Visualization of 3D movement trajectory of second key point of middle finger （countedfrom end of fingertip）"

Fig.9

Visualization of 2D movement trajectory ofsecond key point of middle finger （counted from end of fingertip）"

Fig.10

Visualization of movement parameters of second key point of middle finger（counted from end of fingertip）"

Fig.11

Change of middle finger angle"

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