Skip to main content
SpringerLink
Log in

Hash-based early recognition of gesture patterns

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

In these days, “early recognition” of gesture patterns has been studied by many researchers. Early recognition is a method to make a decision of gesture recognition at the beginning part of it. In traditional method, the key postures for a gesture are utilized for recognition and early recognition is performed frame-by-frame. However, this method has a problem that computational time in recognition processing increases in proportion to size of posture database. If the processing time becomes longer, some input frames will be ignored from the processing. It results in lower recognition accuracy. In this paper, we introduce a hash-based approach to search the posture database. It realizes real-time processing, and keep high performance of recognition.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. This evaluation is defined by,

    $$ \frac{\hbox {\# \; of \; required \; frames}}{\hbox {\# of all frames}}\times 100\,(\%) $$
    (6)

    We utilized this evaluation when the recognition result is output in the gesture. Therefore, the lower value this evaluation takes, the earlier the system can output the recognition result.

References

  1. Starner T, Weaver J, Pentland A (1998) Real-time American sign language recognition using desk and wearable computer based video. IEEE Trans Pattern Anal Mach Intell 20:12

    Google Scholar 

  2. Nickel K, Stiefelhagen R (2007) Visual recognition of pointing gestures for humanrobot interaction. Image Vision Comput 25:1875–1884

    Google Scholar 

  3. Elmezain M, Al-hamadi A, Appenrodt J, Michaelis B (2008) A hidden Markov model-based continuous gesture recognition system for hand motion trajectory. CD-ROM Proc Int Conf Pattern Recognit

  4. Mori A, Uchida S, Kurazume R, Taniguchi R, Hasegawa T, Sakoe H (2012) Early recognition and prediction of gestures. Proc Int Conf Pattern Recognit 3 of 4:560–563

    Google Scholar 

  5. Uchida S, Amamoto K (2008) Early recognition of sequential patterns by classifier combination. CD-ROM Proc Int Conf Pattern Recognit

  6. Kawashima M, Shimada A, Taniguchi R (2012) Early recognition of gesture patterns using self-organizing map. The fifth joint workshop on machine perception and robotics

  7. Indyk P, Motwani R (1998) Approximate nearest neighbor: towards removing the curse of dimensionality. ACM symposium on theory of computing (STOC)

  8. Kohonen T (1989) Self-organization and associative memory. Springer, Berlin

  9. Kohonen T (1995) Self-organizing maps. Springer, Berlin

  10. Microsoft Kinect SDK for Windows. http://www.microsoft.com/en-us/kinectforwindows/

Download references

Acknowledgments

This work was supported by KAKENHI Grant-in-Aid for Young Scientists (A) (23680018).

Author information

Authors and Affiliations

  1. W2-828, 744, Motooka, Nishi-ku, Fukuoka, 819-0395, Japan

    Yoshiyasu Ko, Atsushi Shimada, Hajime Nagahara & Rin-ichiro Taniguchi

Authors
  1. Yoshiyasu Ko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Atsushi Shimada
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hajime Nagahara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rin-ichiro Taniguchi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yoshiyasu Ko.

Additional information

This work was presented in part at the 17th International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 2012.

About this article

Cite this article

Ko, Y., Shimada, A., Nagahara, H. et al. Hash-based early recognition of gesture patterns. Artif Life Robotics 17, 476–482 (2013). https://doi.org/10.1007/s10015-012-0085-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-012-0085-6

Keywords

Search

Navigation