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- Vol. 4 Issue 3
- Article
Yoga Pose Detection System
Authors
1234Dept. Of Computer Science, ITM Gorakhpur, UP, India. 5Asst.Professor, Dept. of Computer Science, ITM Gorakhpur, UP, India.
Article Information
- DOI
- No DOI
- Published In
- Volume 4, Issue 3 (2023)
- Published Online
- May-June 2023
- Pages
- 467-472
- Article Type
- Research Article
- Check for updates
- No DOI available for cross-reference
References
1. A. Agarwal and B. Triggs. (2004). “3D human pose from silhouettes by relevance vector regression”. Intl Conf. on Computer Vision
& Pattern Recogn.pp.882–888.
2. A. Gupta, T. Chen, F. Chen, and D. Kimber. (2011). “Systems and methods for human body pose estimation”. U.S. patent, 7,925,081
B2.
3. A. Kendall, M. Grimes, R. Cipolla. (2015). “PoseNet: a convolutional network for real-time 6DOF camera relocalization”. IEEE Intl.
Conf. Computer Vision.
4. Chen HT, He YZ, Hsu CC et al. (2014). Yoga posture recognition for self-training. pp 496–505.
5. Connaghan D, Kelly P, O’Connor NE et al . (2011). Multi-sensor classification of tennis strokes. . Retrieved from Proc IEEE Sens.:
https://doi.org/ 10.1109/icsens.2011.6127084
6. DatasetOnline. (n.d.). Retrieved from YogaVidCollected: https://archive.org/details
7. E. Trejo, P. Yuan. (2018). “Recognition of yoga poses through an interactive system with kinect device”. Intl. Conf. Robotics and
Automation Science.
8. G. Ning, P. Liu, X. Fan and C. Zhan. (2018). “A top-down approach to articulated human pose estimation and tracking”. ECCV
Workshops.
9. Gao Z, Zhang H, Liu AA et al. (2016). Human action recognition on depth dataset. . Neural Comput Appl 27:2047–2054..
10. H. Sidenbladh, M. Black, and D. Fleet. (2000). “Stochastic tracking of 3D human figures using 2D image motion”. Proc 6th European
Conf. Computer Vision.
11. L.Sigal. (2011). "Human Pose Estimation". Springer: Ency. of Comput.Vision.
12. M. Islam, H. Mahmud, F. Ashraf, I. Hossain and M. Hasan,. ( 2017). "Yoga posture recognition by detecting human joint points in real
time using microsoft kinect". IEEE Region 10 Humanit. Tech. Conf., pp. 668-67.
13. M. Dantone, J. G. (2013). “Human pose estimation using body parts dependent joint regressors”. Proc. IEEE Conf. Computer Vision
Pattern Recogn.
14. P. Dar. (2018). “AI guardman – a machine learning application that uses pose estimation to detect shoplifters”.
15. P. Szczuko. (2019). “Deep neural networks for human pose estimation from a very low resolution depth image” . Multimedia Tools
and Applications, vol. 78, no. 20, pp. 29357–29377.
16. S. Kreiss, L. Bertoni, and A. Alahi. (2019). “PifPaf: composite fields for human pose estimation”. IEEE Conf. Computer Vision and
Pattern Recogn.
17. S. Haque, A. Rabby, M. Laboni, N. Neehal, and S. Hossain,. (2019). “ExNET: deep neural network for exercise pose detection".
Recent Trends in Image Process. and Pattern Recog..
18. S.Yadav, A.Singh,A.Gupta and J.Raheja. (May,2019). "Real Time Yoga Recognition Using Deep Learning". Neural Comput. and Appl.
19. U. Rafi , B. Leibe , J. Gall , and I. Kostrikov. (2016). "An efficient convolutional network for human pose estimation". British Mach.
Vision Conf.
20. Z. Cao, T. Simon, S. Wei, and Y. Sheikh. (2017). “OpenPose: realtime multi-person 2D pose estimation using part affinity fields” .
Proc. 30th IEEE Conf. Computer Vision and Pattern Recogn.
& Pattern Recogn.pp.882–888.
2. A. Gupta, T. Chen, F. Chen, and D. Kimber. (2011). “Systems and methods for human body pose estimation”. U.S. patent, 7,925,081
B2.
3. A. Kendall, M. Grimes, R. Cipolla. (2015). “PoseNet: a convolutional network for real-time 6DOF camera relocalization”. IEEE Intl.
Conf. Computer Vision.
4. Chen HT, He YZ, Hsu CC et al. (2014). Yoga posture recognition for self-training. pp 496–505.
5. Connaghan D, Kelly P, O’Connor NE et al . (2011). Multi-sensor classification of tennis strokes. . Retrieved from Proc IEEE Sens.:
https://doi.org/ 10.1109/icsens.2011.6127084
6. DatasetOnline. (n.d.). Retrieved from YogaVidCollected: https://archive.org/details
7. E. Trejo, P. Yuan. (2018). “Recognition of yoga poses through an interactive system with kinect device”. Intl. Conf. Robotics and
Automation Science.
8. G. Ning, P. Liu, X. Fan and C. Zhan. (2018). “A top-down approach to articulated human pose estimation and tracking”. ECCV
Workshops.
9. Gao Z, Zhang H, Liu AA et al. (2016). Human action recognition on depth dataset. . Neural Comput Appl 27:2047–2054..
10. H. Sidenbladh, M. Black, and D. Fleet. (2000). “Stochastic tracking of 3D human figures using 2D image motion”. Proc 6th European
Conf. Computer Vision.
11. L.Sigal. (2011). "Human Pose Estimation". Springer: Ency. of Comput.Vision.
12. M. Islam, H. Mahmud, F. Ashraf, I. Hossain and M. Hasan,. ( 2017). "Yoga posture recognition by detecting human joint points in real
time using microsoft kinect". IEEE Region 10 Humanit. Tech. Conf., pp. 668-67.
13. M. Dantone, J. G. (2013). “Human pose estimation using body parts dependent joint regressors”. Proc. IEEE Conf. Computer Vision
Pattern Recogn.
14. P. Dar. (2018). “AI guardman – a machine learning application that uses pose estimation to detect shoplifters”.
15. P. Szczuko. (2019). “Deep neural networks for human pose estimation from a very low resolution depth image” . Multimedia Tools
and Applications, vol. 78, no. 20, pp. 29357–29377.
16. S. Kreiss, L. Bertoni, and A. Alahi. (2019). “PifPaf: composite fields for human pose estimation”. IEEE Conf. Computer Vision and
Pattern Recogn.
17. S. Haque, A. Rabby, M. Laboni, N. Neehal, and S. Hossain,. (2019). “ExNET: deep neural network for exercise pose detection".
Recent Trends in Image Process. and Pattern Recog..
18. S.Yadav, A.Singh,A.Gupta and J.Raheja. (May,2019). "Real Time Yoga Recognition Using Deep Learning". Neural Comput. and Appl.
19. U. Rafi , B. Leibe , J. Gall , and I. Kostrikov. (2016). "An efficient convolutional network for human pose estimation". British Mach.
Vision Conf.
20. Z. Cao, T. Simon, S. Wei, and Y. Sheikh. (2017). “OpenPose: realtime multi-person 2D pose estimation using part affinity fields” .
Proc. 30th IEEE Conf. Computer Vision and Pattern Recogn.
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