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Fast Upper Body Joint Tracking Using Kinect Pose Priors

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Articulated Motion and Deformable Objects (AMDO 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8563))

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Abstract

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and instead attempt to incorporate these constraints through priors obtained directly from training data, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this information with a random walk transition model to obtain an upper body model that can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body.

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References

  1. Alspach, D., Sorenson, H.: Nonlinear Bayesian estimation using Gaussian sum approximations. IEEE Transactions on Automatic Control 17(4), 439–448 (1972)

    Article  MATH  Google Scholar 

  2. Charles, J., Pfister, T., Everingham, M., Zisserman, A.: Automatic and efficient human pose estimation for sign language videos. International Journal of Computer Vision (2013)

    Google Scholar 

  3. Chen, R., Liu, J.S.: Mixture Kalman filters. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 62(3), 493–508 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  4. Davison, A.J., Deutscher, J., Reid, I.D.: Markerless motion capture of complex full-body movement for character animation. In: Proceedings of the Eurographic Workshop on Computer Animation and Simulation, pp. 3–14. Springer-Verlag New York, Inc., New York (2001)

    Google Scholar 

  5. Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 126–133 (2000)

    Google Scholar 

  6. Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197–208 (2000)

    Article  Google Scholar 

  7. Eichner, M., Marin-Jimenez, M., Zisserman, A., Ferrari, V.: 2D articulated human pose estimation and retrieval in (almost) unconstrained still images. International Journal of Computer Vision 99, 190–214 (2012)

    Article  MathSciNet  Google Scholar 

  8. Gavrila, D.M., Davis, L.S.: Tracking of humans in action: A 3-D model-based approach. In: Proc. ARPA Image Understanding Workshop, pp. 737–746 (1996)

    Google Scholar 

  9. Germann, M., Popa, T., Ziegler, R., Keiser, R., Gross, M.H.: Space-time body pose estimation in uncontrolled environments. In: 3DIMPVT 2011, pp. 244–251 (2011)

    Google Scholar 

  10. Howe, N.R., Leventon, M.E., Freeman, W.T.: Bayesian reconstruction of 3D human motion from single-camera video. In: Advances in Neural Information Processing Systems. pp. 820–826. MIT Press (1999)

    Google Scholar 

  11. Hua, G., Yang, M., Wu, Y.: Learning to estimate human pose with data driven belief propagation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 747–754 (June 2005)

    Google Scholar 

  12. Kalman, R.E.: A new approach to linear filtering and prediction problems. Transactions of the ASME–Journal of Basic Engineering 82(Series D), 35–45 (1960)

    Google Scholar 

  13. Lee, M.W., Cohen, I.: Human upper body pose estimation in static images. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 126–138. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  14. Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House (2004)

    Google Scholar 

  15. Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: Computer Vision and Pattern Recognition (June 2011)

    Google Scholar 

  16. Sminchisescu, C., Triggs, B.: Covariance-scaled sampling for monocular 3D body tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition, Hawaii, vol. 1, pp. 447–454 (2001)

    Google Scholar 

  17. Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pp. 1385–1392. IEEE Computer Society, Washington, DC (2011)

    Google Scholar 

  18. Yu, T.H., Kim, T.K., Cipolla, R.: Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, pp. 3642–3649. IEEE Computer Society, Washington, DC (2013)

    Google Scholar 

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Burke, M., Lasenby, J. (2014). Fast Upper Body Joint Tracking Using Kinect Pose Priors. In: Perales, F.J., Santos-Victor, J. (eds) Articulated Motion and Deformable Objects. AMDO 2014. Lecture Notes in Computer Science, vol 8563. Springer, Cham. https://doi.org/10.1007/978-3-319-08849-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-08849-5_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08848-8

  • Online ISBN: 978-3-319-08849-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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