Marker-less motion capture system using OpenPose

被引：3
|
作者：
Feng, B. [1 ]
Powell, D. W. [2 ]
Doblas, A. [1 ]
机构：
[1] Univ Memphis, Dept Elect & Comp Engn, Memphis, TN 38152 USA
[2] Univ Memphis, Coll Heath Sci, Memphis, TN 38152 USA
来源：
PATTERN RECOGNITION AND TRACKING XXXIII | 2022年 / 12101卷
关键词：
motion capture system; markerless; body biometrics;
D O I：
10.1117/12.2619059
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Motion capture systems are widely used to measure athletic performance and as a diagnostic tool in sports medicine. Standard motion capture systems record body movement using: (1) a set of cameras to localize body segments; or (2) specialized suits in which inertial measurement units are directly attached to body segments. The major drawbacks of these systems are limited portability, affordability, and accessibility. This contribution presents a markerless motion capture system using a commercially available sports camera and the OpenPose human pose estimation algorithm. We have validated the proposed markerless system by analyzing the human biometrics during running and jumping movements.
引用
收藏
页数：10
相关论文
共 50 条
  • [21] Comparison of marker-based and marker-less systems for low-cost human motion capture
    Puthenveetil, Sajeev C.
    Daphalapurkar, Chinmay P.
    Zhu, Wenjuan
    Leu, Ming C.
    Liu, Xiaoqing F.
    Chang, Alpha M.
    Gilpin-Mcminn, Julie K.
    Wu, Peter H.
    Snodgrass, Scott D.
    Proceedings of the ASME Design Engineering Technical Conference, 2013, 2 B
  • [22] Marker-less Respiratory Motion Modeling Using the Microsoft Kinect for Windows
    Tahavori, F.
    Alnowami, M.
    Wells, K.
    MEDICAL IMAGING 2014: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING, 2014, 9036
  • [23] Marker-less human motion estimation using articulated deformable model
    Ogawara, Koichi
    Li, Xiaolu
    Ikeuchi, Katsushi
    PROCEEDINGS OF THE 2007 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1-10, 2007, : 46 - 51
  • [24] An Evaluation of a 3D Multimodal Marker-Less Motion Analysis System
    Rodrigues, Thiago Braga
    Cathain, Ciaran O.
    Devine, Declan
    Moran, Kieran
    O'Connor, Noel E.
    Murray, Niall
    PROCEEDINGS OF THE 10TH ACM MULTIMEDIA SYSTEMS CONFERENCE (ACM MMSYS'19), 2019, : 213 - 221
  • [25] Objective evaluation of bradykinesia in Parkinson's disease using an inexpensive marker-less motion tracking system
    Lee, Wee Lih
    Sinclair, Nicholas C.
    Jones, Mary
    Tan, Joy L.
    Proud, Elizabeth L.
    Peppard, Richard
    McDermott, Hugh J.
    Perera, Thushara
    PHYSIOLOGICAL MEASUREMENT, 2019, 40 (01)
  • [26] A novel dataset and deep learning-based approach for marker-less motion capture during gait
    Vafadar, Saman
    Skalli, Wafa
    Bonnet-Lebrun, Aurore
    Khalife, Marc
    Renaudin, Mathis
    Hamza, Amine
    Gajny, Laurent
    GAIT & POSTURE, 2021, 86 : 70 - 76
  • [27] Efficient ConvNet-based Marker-less Motion Capture in General Scenes with a Low Number of Cameras
    Elhayek, A.
    de Aguiar, E.
    Jain, A.
    Tompson, J.
    Pishchulin, L.
    Andriluka, M.
    Bregler, C.
    Schiele, B.
    Theobalt, C.
    2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2015, : 3810 - 3818
  • [28] Marker-less 3D feature tracking for mesh-based human motion capture
    de Aguiar, Edilson
    Theobalt, Christian
    Stoll, Carsten
    Seidel, Hans-Peter
    HUMAN MOTION - UNDERSTANDING, MODELING, CAPTURE AND ANIMATION, PROCEEDINGS, 2007, 4814 : 1 - 15
  • [29] Marker-less Tracking for Respiratory Motion Correction in Nuclear Medicine
    Alnowam, Majdi R.
    Lewis, E.
    Guy, M.
    Wells, K.
    2010 IEEE NUCLEAR SCIENCE SYMPOSIUM CONFERENCE RECORD (NSS/MIC), 2010, : 3118 - 3121
  • [30] Mechanical assembly assistance using marker-less augmented reality system
    Wang, Yue
    Zhang, Shusheng
    Yang, Sen
    He, Weiping
    Bai, Xiaoliang
    ASSEMBLY AUTOMATION, 2018, 38 (01) : 77 - 87
12345