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GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking

  • Rune Møllegaard Friborg
  • Søren Hauberg
  • Kenny Erleben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)

Abstract

For many years articulated tracking has been an active research topic in the computer vision community. While working solutions have been suggested, computational time is still problematic. We present a GPU implementation of a ray-casting based likelihood model that is orders of magnitude faster than a traditional CPU implementation. We explain the non-intuitive steps required to attain an optimized GPU implementation, where the dominant part is to hide the memory latency effectively. Benchmarks show that computations which previously required several minutes, are now performed in few seconds.

Keywords

CUDA GPU Computing Articulated Tracking Particle Filtering 

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References

  1. 1.
    Poppe, R.: Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108, 4–18 (2007)CrossRefGoogle Scholar
  2. 2.
    Cappé, O., Godsill, S.J., Moulines, E.: An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE 95, 899–924 (2007)CrossRefGoogle Scholar
  3. 3.
    Sminchisescu, C., Triggs, B.: Kinematic Jump Processes for Monocular 3D Human Tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 69–76 (2003)Google Scholar
  4. 4.
    Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: CVPR, p. 2126. IEEE Computer Society (2000)Google Scholar
  5. 5.
    Hauberg, S., Sommer, S., Pedersen, K.S.: Gaussian-Like Spatial Priors for Articulated Tracking. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 425–437. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Bandouch, J., Beetz, M.: Tracking Humans Interacting with the Environment Using Efficient Hierarchical Sampling and Layered Observation Models. In: IEEE Int. Workshop on Human-Computer Interaction (HCI) (2009)Google Scholar
  7. 7.
    Cabido, R., Concha, D., Pantrigo, J.J., Montemayor, A.S.: High Speed Articulated Object Tracking Using GPUs: A Particle Filter Approach. In: 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks, pp. 757–762. IEEE (2009)Google Scholar
  8. 8.
    Rohr, K.: Towards model-based recognition of human movements in image sequences. CVGIP-Image Understanding 59, 94–115 (1994)CrossRefGoogle Scholar
  9. 9.
    Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic Tracking of 3D Human Figures Using 2D Image Motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    Sengupta, S., Harris, M., Zhang, Y., Owens, J.D.: Scan primitives for gpu computing. In: Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, Aire-la-Ville, Switzerland, pp. 97–106. Eurographics Association (2007)Google Scholar
  11. 11.
    CUDPP: Cuda data parallel primitives library, http://code.google.com/p/cudpp/ (accessed Online April 2010)
  12. 12.
    NVIDIA Corporation: NVIDIA CUDA Best Practices Guide. version 3.0 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rune Møllegaard Friborg
    • 1
  • Søren Hauberg
    • 1
  • Kenny Erleben
    • 1
  1. 1.The eScience Centre, Dept. of Computer ScienceUniversity of CopenhagenDenmark

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