Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 Workshops pp 197–211Cite as

3D Pose Estimation of a Front-Pointing Hand Using a Random Regression Forest

  • Conference paper
  • First Online:

  • 3038 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

Abstract

In this paper, we propose a method for estimating the 3D poses of a front-pointing hand from camera images to realize freehand pointing interaction from a distance. Our method uses a Random Regression Forest (RRF) to realize robust estimation against environmental and individual variations. In order to improve the estimation accuracy, our method supports the use of two cameras and integrates the distributions of the hand poses for these cameras, which are modeled by the Gaussian mixture model. Moreover, tracking of the hand poses further improves the estimation accuracy and stability. The results of performance evaluation showed that the root mean square error of the angle estimation was 4.10\(^{\circ }\), which is accurate enough to expect that our proposed method can be applied to user interface systems.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Leap Motion: https://www.leapmotion.com/

  2. Kölsch, M., Turk, M.: Robust hand detection. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 614–619 (2004)

    Google Scholar 

  3. Song, J., Sörös, G., Pece, F., Fanello, S.R., Izadi, S., Keskin, C., Hilliges, O.: In-air gestures around unmodified mobile devices. In: 27th Annual ACM Symposium on User Interface Software and Technology, pp. 319–329 (2014)

    Google Scholar 

  4. Fanelli, G., Gall, J., Gool, L.V.: Real time head pose estimation with random regression forests. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 617–624 (2011)

    Google Scholar 

  5. Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A.: Efficient regression of general-activity human poses from depth images. In: IEEE International Conference on Computer Vision, pp. 415–422 (2011)

    Google Scholar 

  6. Hara, K., Chellappa, R.: Growing regression forests by classification: applications to object pose estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 552–567. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10605-2_36

    Google Scholar 

  7. Zhen, X., Wang, Z., Yu, M., Li, S.: Supervised descriptor learning for multi-output regression. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1211–1218 (2015)

    Google Scholar 

  8. Oikonomidis, I., Kyriazis, N., Argyros, A.: Efficient model-based 3D tracking of hand articulations using kinect. In: Proceedings of the British Machine Vision Conference, pp. 101.1–101.11 (2011)

    Google Scholar 

  9. Keskin, C., Kıraç, F., Kara, Y.E., Akarun, L.: Hand pose estimation and hand shape classification using multi-layered randomized decision forests. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 852–863. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33783-3_61

    Chapter  Google Scholar 

  10. Tompson, J., Stein, M., Lecun, Y., Perlin, K.: Real-time continuous pose recovery of human hands using convolutional networks. ACM Trans. Graph. 33, 169:1–169:10 (2014)

    Article  Google Scholar 

  11. Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Kim, D., Rhemann, C., Leichter, I., Vinnikov, A., Wei, Y., Freedman, D., Kohli, P., Krupka, E., Fitzgibbon, A., Shahram, I.: Accurate, robust, and flexible real-time hand tracking. In: 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3633–3642 (2015)

    Google Scholar 

  12. Schick, A., van de Camp, F., Ijsselmuiden, J., Stiefelhagen, R.: Extending touch: Towards interaction with large-scale surfaces. In: ACM International Conference on Interactive Tabletops and Surfaces, pp. 117–124 (2009)

    Google Scholar 

  13. Hu, K., Canavan, S., Yin, L.: Hand pointing estimation for human computer interaction based on two orthogonal-views. In: 20th International Conference on Pattern Recognition, pp. 3760–3763 (2010)

    Google Scholar 

  14. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  15. Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends Comput. Graph. Vis. 7, 81–227 (2012)

    Article  MATH  Google Scholar 

  16. Ali-Löytty, S., Niilo, S.: Gaussian mixture filter in hybrid navigation. In: European Navigation Conference, pp. 831–837 (2007)

    Google Scholar 

  17. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, 886–893 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Science and Engineering, Saitama University, Saitama, Japan

    Dai Fujita & Takashi Komuro

Authors
  1. Dai Fujita
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takashi Komuro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Takashi Komuro .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (wmv 18515 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Fujita, D., Komuro, T. (2017). 3D Pose Estimation of a Front-Pointing Hand Using a Random Regression Forest. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54526-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54525-7

  • Online ISBN: 978-3-319-54526-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation