http://iet.metastore.ingenta.com
1887

Deep probabilistic human pose estimation

Deep probabilistic human pose estimation

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The authors consider the problem of human pose estimation using probabilistic convolutional neural networks. They explore ways to improve human pose estimation accuracy on standard pose estimation benchmarks MPII human pose and Leeds Sports Pose (LSP) datasets using frameworks for probabilistic deep learning. Such frameworks transform deterministic neural network into a probabilistic one and allow sampling of independent and equiprobable hypotheses (different outputs) for a given input. Overlapping body parts and body joints hidden under clothes or other obstacles make the problem of human pose estimation ambiguous. In this context to get accurate estimation of joints’ position they use uncertainty in network's predictions, which is represented by variance of hypotheses, provided by a probabilistic convolutional neural network, and confidence is characterised by mean of them. Their work is based on current CNN cascades for pose estimation. They propose and evaluate three probabilistic convolutional neural networks built on top of deterministic ones with two probabilistic deep learning frameworks – DISCO networks and Bayesian SegNet. The authors evaluate their models on standard pose estimation benchmarks and show that proposed probabilistic models outperform base deterministic ones.

References

    1. 1)
      • 1. Johnson, S., Everingham, M.: ‘Clustered pose and nonlinear appearance models for human pose estimation’. Proc. British Machine Vision Conf., Aberystwyth, UK, September 2010, pp. 12.112.11.
    2. 2)
      • 2. Andriluka, M., Pishchulin, L., Gehler, P., et al: ‘2D human pose estimation: New benchmark and state of the art analysis’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Columbus, USA, 2014, pp. 36863693.
    3. 3)
      • 3. Newell, A., Yang, K., Deng, J.: ‘Stacked hourglass networks for human pose estimation’. Proc. European Conf. Computer Vision, Amsterdam, Netherlands, 2016, pp. 483499.
    4. 4)
      • 4. Bouchacourt, D., Mudigonda, P. K., Nowozin, S.: ‘DISCO nets: DISsimilarity COefficients networks’. Advances in Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 352360.
    5. 5)
      • 5. Gal, Y., Ghahramani, Z.: ‘Bayesian convolutional neural networks with Bernoulli approximate variational inference’, CoRR abs/1506.02158, 2015, Pre-Print Version. Available at http://arxiv.org/abs/1506.02158.
    6. 6)
      • 6. Kendall, A., Badrinarayanan, V., Cipolla, R.: ‘Bayesian segnet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding’, CoRR abs/1511.02680, 2015, Pre-Print Version. Available at http://arxiv.org/abs/1511.02680.
    7. 7)
      • 7. Kendall, A., Gal, Y.: ‘What uncertainties do we need in Bayesian deep learning for computer vision?’, CoRR abs/1511.02680, 2017, Pre-Print Version. Available at http://arxiv.org/abs/1703.04977.
    8. 8)
      • 8. Toshev, A., Szegedy, C.: ‘DeepPose: human pose estimation via deep neural networks’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Columbus, USA, 2014, pp. 16531660.
    9. 9)
      • 9. Chen, X., Yuille, A.: ‘Articulated pose estimation by graphical model with image dependent pairwise relations’. Advances in Neural Information Processing Systems, Montreal, Canada, 2014, pp. 17361744.
    10. 10)
      • 10. Karlinsky, L., Ullman, S.: ‘Using linking features in learning non-parametric part models’. Proc. European Conf. Computer Vision, Firenze, Italy, 2012, pp. 326339.
    11. 11)
      • 11. Sun, M., Savarese, S.: ‘Articulated part-based model for joint object detection and pose estimation’. Proc. IEEE Int. Conf. Computer Vision, Barcelona, Spain, 2011, pp. 723730.
    12. 12)
      • 12. Tian, Y., Zitnick, C.L., Narasimhan, S.G.: ‘Exploring the spatial hierarchy of mixture models for human pose estimation’. Proc. European Conf. Computer Vision, Firenze, Italy, 2012, pp. 256269.
    13. 13)
      • 13. Andriluka, M., Roth, S., Schiele, B.: ‘Pictorial structures revisited: people detection and articulated pose estimation’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Miami, USA, 2009, pp. 10141021.
    14. 14)
      • 14. Yang, Y., Ramanan, D.: ‘Articulated human detection with flexible mixtures of parts’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (12), pp. 28782890.
    15. 15)
      • 15. Tompson, J. J., Jain, A., LeCun, Y., et al: ‘Joint training of a convolutional network and a graphical model for human pose estimation’. Advances in Neural Information Processing Systems, Montreal, Canada, 2014, pp. 17991807.
    16. 16)
      • 16. Chen, X., Yuille, A. L.: ‘Articulated pose estimation by a graphical model with image dependent pairwise relations’. Advances in Neural Information Processing Systems, Montreal, Canada, 2014, pp. 17361744.
    17. 17)
      • 17. Pishchulin, L., Insafutdinov, E., Tang, S., et al: ‘DeepCut: joint subset partition and labeling for multi person pose estimation’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, USA, 2016, pp. 49294937.
    18. 18)
      • 18. Insafutdinov, E., Pishchulin, L., Andres, B., et al: ‘Deepercut: A deeper, stronger, and faster multi-person pose estimation model’. Proc. European Conf. Computer Vision, Amsterdam, the Netherlands, 2016, pp. 3450.
    19. 19)
      • 19. Wei, S. E., Ramakrishna, V., Kanade, T., et al: ‘Convolutional pose machines’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, USA, 2016, pp. 47244732.
    20. 20)
      • 20. Srivastava, N., Hinton, G.E., Krizhevsky, A., et al: ‘Dropout: a simple way to prevent neural networks from overfitting’, J. Mach. Learn. Res., 2014, 15, (1), pp. 19291958.
    21. 21)
      • 21. Johnson, S., Everingham, M.: ‘Learning effective human pose estimation from inaccurate annotation’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Colorado, USA, 2011, pp. 14651472.
    22. 22)
      • 22. Tompson, J., Goroshin, R., Jain, A., et al: ‘Efficient object localization using convolutional networks’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, Boston, USA, 2015, pp. 648656.
    23. 23)
      • 23. Abadi, M., Agarwal, A., Barham, P., et al: ‘Tensorflow: large-scale machine learning on heterogeneous distributed systems’, CoRR abs/1603.04467, 2016, Pre-Print Version. Available at http://arxiv.org/abs/1603.04467.
    24. 24)
      • 24. Tieleman, T., Hinton, G.: ‘Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude’, COURSERA: Neural Netw. Mach. Learn., 2012, 4, (2), pp. 2631.
    25. 25)
      • 25. Shalnov, E., Konushin, A.: ‘Human pose estimation in video via MCMC sampling’, Proc. 5th Int. Workshop on Image Mining. Theory and Applications, 2015, vol. 1, pp. 7179.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0382
Loading

Related content

content/journals/10.1049/iet-cvi.2017.0382
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address