Advertisement

Fingertips Tracking Based Active Contour for General HCI Application

  • Kittasil SilanonEmail author
  • Nikom Suvonvorn
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

This paper presents a real time estimation method for 3D trajectory of fingertips. Our approach is based on depth vision, with Kinect depth sensor. The hand is extracted using hand detector and depth image from sensor. The fingertips are located by the analysis of the curvature of hand contour. The fingertips detector is implemented using concept of active contour which combine the energy of continuity, curvature, direction, depth and distance. The trajectory of fingertips is filtered to reduce the tracking error. The experiment is evaluated on the fingers movement sequences. Besides, the capabilities of the method are demonstrated on the real-time Human–Computer Interaction (HCI) application.

Keywords

We fingertips detection and tracking Hand posture estimation Human–computer interaction (HCI) 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

We would like to thank the National Research University Project of Thailand’s Office of the Higher Education Commission for financial support

References

  1. 1.
    Deng, J.W., Tsui, H.T.: An HMM-based approach for gesture segmentation and recognition. In: 15th International Conference on Pattern Recognition Proceedings, vol. 3, pp. 679-682 (2000)Google Scholar
  2. 2.
    Ho-Sub Yoon, Jung Soh, Younglae J. Bae, Hyun Seung Yang: Hand gesture recognition using combined features of location, angle and velocity. In: Pattern Recognition, vol. 34, pp. 1491–1501 (2001)Google Scholar
  3. 3.
    Feng-Sheng Chen, Chih-Ming Fu, Chung-Lin Huang: Hand gesture recognition using a real-time tracking method and hidden Markov models. In: Image and Vision Computing, vol. 21, pp. 745–758 (2003)Google Scholar
  4. 4.
    Elmezain M., Al-Hamadi: Gesture Recognition for Alphabets from Hand Motion Trajectory Using Hidden Markov Model. In: IEEE International Symposium on Signal Processing and information Technology, pp.1192-1197 (2007)Google Scholar
  5. 5.
    Kittasil Silanon, Nikom Suvonvorn: Hand Motion Analysis for Thai Alphabet Recognition using HMM. In: International Journal of Information and Electronics Engineering vol. 1, pp. 65-71 (2011)Google Scholar
  6. 6.
    Wei Du, Hua Li: Vision based gesture recognition system with single camera. In: 5th International Conference on Signal Processing Proceedings WCCC-ICSP, vol. 2, pp.1351-1357 (2000)Google Scholar
  7. 7.
    Antonis A. Argyros, Manolis I. A. Lourakis: Vision-based interpretation of hand gestures for remote control of a computer mouse. In: Computer Vision in Human-Computer Interaction, pp. 40-51 (2006)Google Scholar
  8. 8.
    Ko-Jen Hsiao, Tse-Wei Chen, Shao-Yi Chien: Fast fingertip positioning by combining particle filtering with particle random diffusion. In: IEEE International Conference on Multimedia and Expo, pp. 977-980 (2008)Google Scholar
  9. 9.
    J. Ravikiran, Mahesh Kavi, Mahishi Suhas, R. Dheeraj, S. Sudheender, Pujari Nitin V.: Finger Detection for Sign Language Recognition In: International MultiConference of Engineers & Computer Scientists, pp. 489 (2009)Google Scholar
  10. 10.
    Lee, J., Kunii, T.L.: Model-based analysis of hand posture. In: IEEE Computer Graphics and Applications, vol.15, no.5, pp.77-86 (1995)Google Scholar
  11. 11.
    Cheng-Chang Lien, Chung-Lin Huang: Model-based articulated hand motion tracking for gesture recognition. In: Image and Vision Computing, vol. 16, Issue 2, pp. 121-134, (February 1998)Google Scholar
  12. 12.
    Lathuiliere, F., Herve, J. Y.: Visual tracking of hand posture with occlusion handling. In: 15th International Conference on Pattern Recognition Proceedings, vol.3, pp.1129-1133 (2000)Google Scholar
  13. 13.
    Dung Duc Nguyen, Thien Cong Pham, Jae Wook Jeon: Fingertip detection with morphology and geometric calculation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1460-1465 (2009)Google Scholar
  14. 14.
    M. Do, T. Asfour, R. Dillman: Partical filter-based fingertips tracking with circular hough transform feature. In: Proceedings of the 12th IAPR Conference on Machine Vision Application (2011)Google Scholar
  15. 15.
    Raheja, J.L., Chaudhary, A., Singal, K.: Tracking of Fingertips and Centers of Palm Using KINECT Computational Intelligence. Third International Conference on Modeling and Simulation (CIMSiM), pp. 248-252 (2011)Google Scholar
  16. 16.
    Hui Liang, Junsong Yuan, Daniel Thalmann: 3D fingertip and palm tracking in depth image sequences. In: Proceedings of the 20th ACM international conference on Multimedia (MM ‘12). ACM, New York, NY, USA, pp.785-788 (2012)Google Scholar
  17. 17.
  18. 18.
    Michael Kass, Andrew Witkin, Demetri Terzopoulos: Snakes: Active contour models. In: International journal of computer vision, vol. 1, no. 4, pp. 321-331 (1988)Google Scholar
  19. 19.
    Donna J. Williams, Mubarak Shah: A Fast algorithm for active contours and curvature estimation. In: CVGIP: Image Understanding, vol. 55, no. 1, pp. 14-26 (January 1992)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of EngineeringPrince of Songkla UniversityHat YaiThailand

Personalised recommendations