Multi-View 3D Human Pose Tracking Based on Evolutionary Robot Vision

Wei Quan; Naoyuki Kubota

doi:10.20965/jaciii.2021.p0432

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JACIII Vol.25 No.4 pp. 432-441

doi: 10.20965/jaciii.2021.p0432

(2021)

Paper:

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Multi-View 3D Human Pose Tracking Based on Evolutionary Robot Vision

Wei Quan and Naoyuki Kubota

Graduate School of System Design, Tokyo Metropolitan University
2-6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

Received:

March 2, 2021

Accepted:

April 30, 2021

Published:

July 20, 2021

Keywords:

three-dimensional pose estimation, evolutionary algorithm, physical exercise monitoring

Abstract

Human life expectancy is at present the maximum in recorded history. However, a disadvantage is that the elderly are increasingly displaying cognitive disabilities. Studies have shown that physical exercises such as calisthenics can potentially prevent disabilities. Meanwhile, existing systems for evaluating human pose focus mainly on accuracy and omit convenience and efficiency. To solve this issue, in this paper, we propose a framework for rapidly estimating three-dimensional human pose from two camera views. It is based on an evolutionary algorithm. This system can be applied straightforwardly to inexpensive smart devices and used to evaluate multiple individuals’ calisthenics with two or more smart devices.

Cite this article as:

W. Quan and N. Kubota, “Multi-View 3D Human Pose Tracking Based on Evolutionary Robot Vision,” J. Adv. Comput. Intell. Intell. Inform., Vol.25 No.4, pp. 432-441, 2021.

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References

[1] D. L. Murman, “The Impact of Age on Cognition,” Seminars in Hearing, Vol.36, pp. 111-121, doi:10.1055/s-0035-1555115, 2015.
[2] N. J. Kirk-Sanchez and E. L. McGough, “Physical exercise and cognitive performance in the elderly: current perspectives,” Clinical Interventions in Aging, Vol.9, pp. 51-62, doi: 10.2147/CIA.S39506, 2014.
[3] R. Poppe, “A survey on vision-based human action recognition,” Image and Vision Computing, Vol.28, Issue 6, pp. 976-990, doi: 10.1016/j.imavis.2009.11.014, 2010.
[4] M. Eichner and V. Ferrari, “Human Pose Co-Estimation and Applications,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.34, No.11, pp. 2282-2288, doi: 10.1109/TPAMI.2012.85, 2012.
[5] T. B. Moeslund and E. Granum, “A Survey of Computer Vision-Based Human Motion Capture,” Computer Vision and Image Understanding, Vol.81, Issue 3, pp 231-268, 2001.
[6] H. Jiang, “Human Pose Estimation Using Consistent Max Covering,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.33, No.9, pp. 1911-1918, doi: 10.1109/TPAMI.2011.92, 2011.
[7] L. Lo Presti and M. La Cascia, “3D skeleton-based human action classification: A survey,” Pattern Recognition, Vol.53, pp. 130-147, 2016.
[8] J. Shotton, “Real-Time Human Pose Recognition in Parts from a Single Depth Image,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1297-1304, 2011.
[9] V. Belagiannis, S. Amin, M. Andriluka, B. Schiele, N. Navab, and S. Ilic, “3D Pictorial Structures for Multiple Human Pose Estimation,” 2014 IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1669-1676, doi: 10.1109/CVPR.2014.216, 2014.
[10] Y. Chen, Y. Tian, and M. He, “Monocular Human Pose Estimation: A Survey of Deep Learning-Based Methods,” arXiv preprint, arXiv:2006.01423, 2020.
[11] A. Kendall, M. Grimes, and R. Cipolla, “PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization,” The IEEE Int. Conf. on Computer Vision (ICCV), doi: 10.1109/ICCV.2015.336, 2015.
[12] Y. Chen, C. Shen, X.-S. Wei, L. Liu, and J. Yang, “Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation,” The IEEE Int. Conf. on Computer Vision (ICCV), doi: 10.1109/ICCV.2017.137, 2017.
[13] D. Metaxas and D. Terzopoulos, “Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.15, No.6, pp. 580-591, 1993.
[14] R. I. Hartley and P. Sturm, “Triangulation,” Proc. ARPA Image Understanding Workshop, pp. 957-966, 1994.
[15] J. H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence,” MIT Press, 1992.
[16] Y. Zhang, S. Wang, and G. Ji, “A comprehensive survey on particle swarm optimization algorithm and its applications,” Mathematical Problems in Engineering, Vol.2015, Article No.931256, 2015.
[17] W. Quan, J. Woo, Y. Toda, and N. Kubota, “Human Posture Recognition for Estimation of Human Body Condition,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.3, pp. 519-527, doi: 10.20965/jaciii.2019.p0519, 2019.
[18] J. Denavit and R. Hartenberg, “A kinematic notation for lower-pair mechanisms based on matrices,” Trans ASME: J. of Applied Mechanics, Vol.23, pp. 215-221, 1955.
[19] R. S. Hartenberg and J. Denavit, “Kinematic synthesis of linkages,” McGraw-Hill Series in Mechanical Engineering, 435pp., McGraw-Hill, 1965.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] D. L. Murman, “The Impact of Age on Cognition,” Seminars in Hearing, Vol.36, pp. 111-121, doi:10.1055/s-0035-1555115, 2015.

[2] [2] N. J. Kirk-Sanchez and E. L. McGough, “Physical exercise and cognitive performance in the elderly: current perspectives,” Clinical Interventions in Aging, Vol.9, pp. 51-62, doi: 10.2147/CIA.S39506, 2014.

[3] [3] R. Poppe, “A survey on vision-based human action recognition,” Image and Vision Computing, Vol.28, Issue 6, pp. 976-990, doi: 10.1016/j.imavis.2009.11.014, 2010.

[4] [4] M. Eichner and V. Ferrari, “Human Pose Co-Estimation and Applications,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.34, No.11, pp. 2282-2288, doi: 10.1109/TPAMI.2012.85, 2012.

[5] [5] T. B. Moeslund and E. Granum, “A Survey of Computer Vision-Based Human Motion Capture,” Computer Vision and Image Understanding, Vol.81, Issue 3, pp 231-268, 2001.

[6] [6] H. Jiang, “Human Pose Estimation Using Consistent Max Covering,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.33, No.9, pp. 1911-1918, doi: 10.1109/TPAMI.2011.92, 2011.

[7] [7] L. Lo Presti and M. La Cascia, “3D skeleton-based human action classification: A survey,” Pattern Recognition, Vol.53, pp. 130-147, 2016.

[8] [8] J. Shotton, “Real-Time Human Pose Recognition in Parts from a Single Depth Image,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1297-1304, 2011.

[9] [9] V. Belagiannis, S. Amin, M. Andriluka, B. Schiele, N. Navab, and S. Ilic, “3D Pictorial Structures for Multiple Human Pose Estimation,” 2014 IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1669-1676, doi: 10.1109/CVPR.2014.216, 2014.

[10] [10] Y. Chen, Y. Tian, and M. He, “Monocular Human Pose Estimation: A Survey of Deep Learning-Based Methods,” arXiv preprint, arXiv:2006.01423, 2020.

[11] [11] A. Kendall, M. Grimes, and R. Cipolla, “PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization,” The IEEE Int. Conf. on Computer Vision (ICCV), doi: 10.1109/ICCV.2015.336, 2015.

[12] [12] Y. Chen, C. Shen, X.-S. Wei, L. Liu, and J. Yang, “Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation,” The IEEE Int. Conf. on Computer Vision (ICCV), doi: 10.1109/ICCV.2017.137, 2017.

[13] [13] D. Metaxas and D. Terzopoulos, “Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.15, No.6, pp. 580-591, 1993.

[14] [14] R. I. Hartley and P. Sturm, “Triangulation,” Proc. ARPA Image Understanding Workshop, pp. 957-966, 1994.

[15] [15] J. H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence,” MIT Press, 1992.

[16] [16] Y. Zhang, S. Wang, and G. Ji, “A comprehensive survey on particle swarm optimization algorithm and its applications,” Mathematical Problems in Engineering, Vol.2015, Article No.931256, 2015.

[17] [17] W. Quan, J. Woo, Y. Toda, and N. Kubota, “Human Posture Recognition for Estimation of Human Body Condition,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.3, pp. 519-527, doi: 10.20965/jaciii.2019.p0519, 2019.

[18] [18] J. Denavit and R. Hartenberg, “A kinematic notation for lower-pair mechanisms based on matrices,” Trans ASME: J. of Applied Mechanics, Vol.23, pp. 215-221, 1955.

[19] [19] R. S. Hartenberg and J. Denavit, “Kinematic synthesis of linkages,” McGraw-Hill Series in Mechanical Engineering, 435pp., McGraw-Hill, 1965.