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Fast Human Pose Tracking with a Single Depth Sensor Using Sum of Gaussians Models

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Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

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Abstract

We introduce a simple yet effective 3D human pose tracking from a single depth sensor by using the Sum of Gaussians (SoG) models. Both the human body model and the point cloud converted from a depth map are represented by two different SoG models, which allow us to compute and optimize their similarity analytically. We have two main contributions in this work. The first is we extend the SoG-based similarity by integrating two additional terms to enhance the robustness and accuracy of 3D pose tracking. One is a visibility term to handel the incomplete data problem and the other is a continuity term to smooth the motion estimation. Second, we develop a validation and re-initialization strategy to detect and recover tracking failures. Our algorithm is practically promising that neither involves training data nor a detailed mesh or complicated 3D model. The experimental results are impressing and competitive when compared with state-of-the-art algorithms on a benchmark dataset considering the efficiency and simplicity of our method.

This work is supported by Oklahoma Center for the Advancement of Science and Technology (OCAST) under grants HR09-030 and HR12-30.

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References

  1. 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: Proceedings of CVPR (2011)

    Google Scholar 

  2. Ganapathi, V., Plagemann, C., Koller, D., Thrun, S.: Real-time human pose tracking from range data. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 738–751. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  3. Gall, J., Stoll, C., de Aguiar, E., Theobalt, C., et al.: Motion capture using joint skeleton tracking and surface estimation. In: Proceedings of CVPR (2009)

    Google Scholar 

  4. Ye, M., Yang, R.: Real-time simultaneous pose and shape estimation for articulated objects with a single depth camera. In: Proceedings of CVPR (2014)

    Google Scholar 

  5. Baak, A., Muller, M., Bharaj, G., et al.: A data-driven approach for real-time full body pose reconstruction from a depth camera. In: Proceedings of ICCV (2011)

    Google Scholar 

  6. Ye, M., Wang, X., Yang, R., Ren, L., Pollefeys, M.: Accurate 3D pose estimation from a single depth image. In: Proceedings of ICCV (2011)

    Google Scholar 

  7. Stoll, C., Hasler, N., Gall, J., Seidel, H.P., Theobalt, C.: Fast articulated motion tracking using a sums of Gaussians body model. In: Proceedings of ICCV (2011)

    Google Scholar 

  8. Kurmankhojayev, D., Hasler, N., et al.: Monocular pose capture with a depth camera using a Sums-of-Gaussians body model. In: Pattern Recognition (2013)

    Google Scholar 

  9. Sridhar, S., Oulasvirta, A., Theobalt, C.: Interactive markerless articulated hand motion tracking using RGB and depth data. In: Proceedings of ICCV (2013)

    Google Scholar 

  10. Ganapathi, V., Plagemann, C., Koller, D., Thrun, S.: Real time motion capture using a single time-of-flight camera. In: Proceedings of CVPR (2010)

    Google Scholar 

  11. Helten, T., Muller, M., Seidel, H.P., Theobalt, C.: Real-time body tracking with one depth camera and inertial sensors. In: Proceedings of ICCV (2013)

    Google Scholar 

  12. Taylor, J., Shotton, J., et al.: The vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation. In: Proceedings of CVPR (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electrical and Computer Engineering, Oklahoma State University, USA

    Meng Ding & Guoliang Fan

Authors
  1. Meng Ding
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  2. Guoliang Fan
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Santa Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

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© 2014 Springer International Publishing Switzerland

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Ding, M., Fan, G. (2014). Fast Human Pose Tracking with a Single Depth Sensor Using Sum of Gaussians Models. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_57

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  • DOI: https://doi.org/10.1007/978-3-319-14249-4_57

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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