Skip to main content
SpringerLink
Log in
Book cover

International Conference on Interactive Collaborative Robotics

ICR 2018: Interactive Collaborative Robotics pp 259–269Cite as

Robust Webcam-Based Hand Detection for Initialisation of Hand-Gesture Communication

  • Conference paper
  • First Online:

  • 1376 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11097))

Abstract

The recognition of hand gestures is still a challenging task in real-life scenarios, especially when the hardware is restricted to a cheap optical camera. The first step in such systems is to find at least one hand that can be tracked in order to identify postures or gestures. We propose a robust and real-time method that is able to reliably detect the hand in various environments to initialize hand-gesture communication. It is based on an innovative combination of different sources of information (colour, motion, trajectory) and a dynamic hand-wave gesture commencing hand tracking and hand gesture recognition.

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

Notes

  1. 1.

    During the hand-wave gesture, it can be assumed that the hand is presented with fingers pointing upwards.

  2. 2.

    Keep in mind that the vector magnitudes are often close to zero for inner hand parts.

References

  1. Premaratne, P.: Human Computer Interaction Using Hand Gestures. Springer, Singapore (2014). https://doi.org/10.1007/978-981-4585-69-9

    Book  Google Scholar 

  2. Langmann, B., Hartmann, K., Loffeld, O.: Depth camera technology comparison and performance evaluation. In: Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, pp. 438–444 (2012)

    Google Scholar 

  3. Triesch, J., von der Malsburg, C.: Robust classification of hand postures against complex background. In: Proceedings of 2nd International Conference on Automatic Face and Gesture Recognition, pp. 14–16, October 1996

    Google Scholar 

  4. Chen, F.-S., Fu, C.-M., Huang, C.-L.: Hand gesture recognition using a real-time tracking method and hidden Markov models. Image Vis. Comput. 21(8), 745–758 (2003)

    Article  Google Scholar 

  5. Mittal, A., Zisserman, A., Torr, P.H.: Hand detection using multiple proposals. In: BMVC, pp. 1–11 (2011)

    Google Scholar 

  6. Stergiopoulou, E., Sgouropoulos, K., Nikolaou, N., Papamarkos, N., Mitianoudis, N.: Real time hand detection in a complex background. Engin. Appl. Artif. Intell. 35, 54–70 (2014)

    Article  Google Scholar 

  7. Deng, X., Zhang, Y., Yang, S., Tan, P., Chang, L., Yuan, Y., Wang, H.: Joint hand detection and rotation estimation using CNN. IEEE Trans. Image Process. 27(4), 1888–1900 (2018)

    Article  MathSciNet  Google Scholar 

  8. Bambach, S., Lee, S., Crandall, D.J., Yu, C.: Lending a hand: detecting hands and recognizing activities in complex egocentric interactions. In: IEEE International Conference on Computer Vision (ICCV), pp. 1949–1957, December 2015

    Google Scholar 

  9. Palacios, J.M., Sagüs, C., Montijano, E., Llorente, S.: Human-computer interaction based on hand gestures using RGB-D sensors. Sensors 13(9), 11842–11860 (2013)

    Article  Google Scholar 

  10. Kawulok, M., Nalepa, J., Kawulok, J.: Skin detection and segmentation in color images. In: Celebi, M.E., Smolka, B. (eds.) Advances in Low-Level Color Image Processing. LNCVB, vol. 11, pp. 329–366. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-007-7584-8_11

    Chapter  Google Scholar 

  11. Phung, S.L., Bouzerdoum, A., Chai, D.: Skin segmentation using color pixel classification: analysis and comparison. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 148–154 (2005)

    Article  Google Scholar 

  12. Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_50

    Chapter  Google Scholar 

  13. Kapur, J.N., Sahoo, P.K., Wong, A.K.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Grap. Image Process. 29(3), 273–285 (1985)

    Article  Google Scholar 

  14. Wang, B., Xu, J.: Accurate and fast hand-forearm segmentation algorithm based on silhouette. In: 2012 IEEE 2nd International Conference on Cloud Computing and Intelligent Systems (CCIS), vol. 2. IEEE (2012)

    Google Scholar 

  15. Chai, X., Fang, Y., Wang, K.: Robust hand gesture analysis and application in gallery browsing. In: IEEE International Conference on Multimedia and Expo, ICME 2009, pp. 938–941. IEEE (2009)

    Google Scholar 

  16. http://www1.hft-leipzig.de/strutz/Papers/RoHaDe-resources/. Accessed 13 June 2018

Download references

Author information

Authors and Affiliations

  1. Institute of Communications, Leipzig University of Telecommunications (HfTL), Leipzig, Germany

    Tilo Strutz, Alexander Leipnitz & Björn Senkel

Authors
  1. Tilo Strutz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Leipnitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Björn Senkel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tilo Strutz .

Editor information

Editors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia

    Andrey Ronzhin

  2. TU Munich, Munich, Germany

    Gerhard Rigoll

  3. Institute for Control Problem of the Russian Academy of Sciences, Moscow, Russia

    Roman Meshcheryakov

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Strutz, T., Leipnitz, A., Senkel, B. (2018). Robust Webcam-Based Hand Detection for Initialisation of Hand-Gesture Communication. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2018. Lecture Notes in Computer Science(), vol 11097. Springer, Cham. https://doi.org/10.1007/978-3-319-99582-3_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99582-3_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99581-6

  • Online ISBN: 978-3-319-99582-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation