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Robust Pose Recognition Using Deep Learning

  • Aparna MohantyEmail author
  • Alfaz Ahmed
  • Trishita Goswami
  • Arpita Das
  • Pratik Vaishnavi
  • Rajiv Ranjan Sahay
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)

Abstract

Current pose estimation methods make unrealistic assumptions regarding the body postures. Here, we seek to propose a general scheme which does not make assumptions regarding the relative position of body parts. Practitioners of Indian classical dances such as Bharatnatyam often enact several dramatic postures called Karanas. However, several challenges such as long flowing dresses of dancers, occlusions, change of camera viewpoint, poor lighting etc. affect the performance of state-of-the-art pose estimation algorithms [1, 2] adversely. Body postures enacted by practitioners performing Yoga also violate the assumptions used in current techniques for estimating pose. In this work, we adopt an image recognition approach to tackle this problem. We propose a dataset consisting of 864 images of 12 Karanas captured under controlled laboratory conditions and 1260 real-world images of 14 Karanas obtained from Youtube videos for Bharatnatyam. We also created a new dataset consisting of 400 real-world images of 8 Yoga postures. We use two deep learning methodologies, namely, convolutional neural network (CNN) and stacked auto encoder (SAE) and demonstrate that both these techniques achieve high recognition rates on the proposed datasets.

Keywords

Pose estimation Deep learning Convolutional neural network Stacked auto encoder 

References

  1. 1.
    Y. Yang and D. Ramanan, “Articulated pose estimation with flexible mixtures-of-parts,” in CVPR, 2011, pp. 1385–1392.Google Scholar
  2. 2.
    F. Wang and Y. Li, “Beyond physical connections: Tree models in human pose estimation,” in CVPR, 2013, pp. 596–603.Google Scholar
  3. 3.
    D. Ramanan, “Learning to parse images of articulated bodies,” in NIPS, 2006, pp. 1129–1136.Google Scholar
  4. 4.
    A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NIPS, 2012, pp. 1097–1105.Google Scholar
  5. 5.
    Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in Proceedings of the IEEE, 1998, pp. 2278–2324.Google Scholar
  6. 6.
    A. Mallik, S. Chaudhury, and H. Ghosh, “Nrityakosha: Preserving the intangible heritage of indian classical dance,” Journal on Computing and Cultural Heritage (JOCCH), vol. 4, no. 3, p. 11, 2011.Google Scholar
  7. 7.
    S. Samanta, P. Purkait, and B. Chanda, “Indian classical dance classification by learning dance pose bases,” in IEEE Workshop on Applications of Computer Vision (WACV), 2012, pp. 265–270.Google Scholar
  8. 8.
    J. O’Rourke and N. Badler, “Model-based image analysis of human motion using constraint propagation,” IEEE Trans. PAMI, vol. 2, no. 6, pp. 522–536, Nov 1980.Google Scholar
  9. 9.
    G. Mori and J. Malik, “Estimating human body configurations using shape context matching,” in ECCV, ser. 02, 2002, pp. 666–680.Google Scholar
  10. 10.
    P. Felzenszwalb, D. McAllester, and D. Ramanan, “A discriminatively trained, multiscale, deformable part model,” in CVPR, 2008, pp. 1–8.Google Scholar
  11. 11.
    P. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” PAMI, vol. 32, no. 9, pp. 1627–1645, 2010.CrossRefGoogle Scholar
  12. 12.
    P. F. Felzenszwalb and D. P. Huttenlocher, “Pictorial structures for object recognition,” Intnl. Jrnl. Comp. Vis., vol. 61, no. 1, pp. 55–79, 2005.CrossRefGoogle Scholar
  13. 13.
    M. Andriluka, S. Roth, and B. Schiele, “Pictorial structures revisited: People detection and articulated pose estimation,” in CVPR, 2009, pp. 1014–1021.Google Scholar
  14. 14.
    A. Toshev and C. Szegedy, “Deeppose: Human pose estimation via deep neural networks,” in CVPR, 2014, pp. 1653–1660.Google Scholar
  15. 15.
    L. Pishchulin, M. Andriluka, P. Gehler, and B. Schiele, “Poselet conditioned pictorial structures,” in CVPR, 2013, pp. 588–595.Google Scholar
  16. 16.
    Y. Tian, C. L. Zitnick, and S. G. Narasimhan, “Exploring the spatial hierarchy of mixture models for human pose estimation,” in Computer Vision–ECCV 2012.   Springer, 2012, pp. 256–269.Google Scholar
  17. 17.
    M. Dantone, J. Gall, C. Leistner, and L. Van Gool, “Human pose estimation using body parts dependent joint regressors,” in CVPR, 2013, pp. 3041–3048.Google Scholar
  18. 18.
    L. Pishchulin, A. Jain, M. Andriluka, T. Thormahlen, and B. Schiele, “Articulated people detection and pose estimation: Reshaping the future,” in CVPR, 2012, pp. 3178–3185.Google Scholar
  19. 19.
    M. Eichner, M. Marin-Jimenez, A. Zisserman, and V. Ferrari, “2D articulated human pose estimation and retrieval in (almost) unconstrained still images,” IJCV, vol. 99, no. 2, pp. 190–214, 2012.MathSciNetCrossRefGoogle Scholar
  20. 20.
    A. Agarwal and B. Triggs, “Recovering 3D human pose from monocular images,” IEEE Trans. PAMI, vol. 28, no. 1, pp. 44–58, 2006.CrossRefGoogle Scholar
  21. 21.
    J. Shotton, T. Sharp, A. Kipman, A. Fitzgibbon, M. Finocchio, A. Blake, M. Cook, and R. Moore, “Real-time human pose recognition in parts from single depth images,” Communications of the ACM, vol. 56, no. 1, pp. 116–124, 2013.CrossRefGoogle Scholar
  22. 22.
    J. Xie, L. Xu, and E. Chen, “Image denoising and inpainting with deep neural networks,” in NIPS, 2012, pp. 341–349.Google Scholar
  23. 23.
    R. B. Palm, “Prediction as a candidate for learning deep hierarchical models of data,” Master’s thesis, 2012. [Online]. Available: https://github.com/rasmusbergpalm/DeepLearnToolbox

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Aparna Mohanty
    • 1
    Email author
  • Alfaz Ahmed
    • 2
  • Trishita Goswami
    • 2
  • Arpita Das
    • 2
  • Pratik Vaishnavi
    • 3
  • Rajiv Ranjan Sahay
    • 1
  1. 1.Department of Electrical EngineeringIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Department of Computer Science and TechnologyIndian Institute of Engineering Science and TechnologyShibpurIndia
  3. 3.Sardar Vallabhai National Institute of Technology SuratSuratIndia

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