Skip to main content
SpringerLink
Log in

A Real-Time Head Tracker Supporting Human Computer Interaction

  • Conference paper
Computer Recognition Systems

Part of the book series: Advances in Soft Computing ((AINSC,volume 30))

Abstract

This paper describes a fast and completely automatic algorithm for human face tracking. The tracked face is represented by a weighted histogram. The current histogram is compared to histograms at the particles’ positions. The weight of each particle is determined on the basis of Bhattacharyya distance and intensity gradient along the ellipse’s boundary. The incorporation of information about the distance between the camera and the face undergoing tracking results in robust tracking even in presence of skin colored regions in the background. The initialization of the tracker is realized by means of face detection. The detection is carried out using Haar-like features, followed by the verification of face distance to the camera and face region size heuristics.

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

Access this chapter

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 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Birchfield S. (1998) Elliptical head tracking using intensity gradients and color histograms, The IEEE Conf. tion Comp. Vision and Patt. Rec., 232–237

    Google Scholar 

  2. Bradski G. R. (1998) Computer vision face tracking as a component of a perceptual user interface, In Workshop on Applications of Computer Vision, 214–219

    Google Scholar 

  3. Chen Y., Rui Y., Huang T. (2002) Mode-based multi-hypothesis head tracking using parametric contours, In Proc. IEEE Int. Conf. on Aut. Face Rec., 112–117

    Google Scholar 

  4. Comaniciu D., Ramesh V., Meer P. (2000) Real-time tracking of non-rigid objects using Mean Shift, The IEEE Conf. on Comp. Vision and Patt. Rec., 142–149

    Google Scholar 

  5. Darrell T., Gordon G., Harville M., Woodfill J. (1998) Integrated person tracking using stereo, color, and pattern detection, Proc. of the Conf. on Comp. Vision and Patt. Rec., 601–609

    Google Scholar 

  6. Doucet A., Godsill S., Andrieu Ch. (2000) On sequential Monte Carlo sampling methods for bayesian filtering, Statistics and Computing, 10:197–208

    Article  Google Scholar 

  7. Isard M., Blake A. (1998) CONDENSATION-conditional density propagation for visual tracking, Int. J. of Computer Vision, 29:5–28

    Article  Google Scholar 

  8. Konolige K. (1997) Small Vision System: Hardware and implementation, Proc. of Int. Symp. on Robotics Research, 111–116

    Google Scholar 

  9. Kwolek B. (2004) Stereovision-based head tracking using color and ellipse fitting in a particle filter, 8th European Conf. on Computer Vision, 192–204

    Google Scholar 

  10. Perez P., Hue C., Vermaak J., Gangnet M. (2002) Color-based probabilistic tracking, European Conf. on Computer Vision, 661–675

    Google Scholar 

  11. Pioneer 2 mobile robots (2001) ActivMedia Robotics

    Google Scholar 

  12. Rowley H., Baluja S., Kanade T. (1996) Neural network-based face detection, Proc. of IEEE Conf. on Comp. Vision and Patt. Rec., 203–207

    Google Scholar 

  13. Schapire R., Singer Y. (1998) Improved boosting algorithms using confidencerated predictions, Proc. 11th Ann. Conf. Computational Learning Theory, 80–91

    Google Scholar 

  14. Swain M. J., Ballard D. H. (1991) Color indexing, Int. J. of Computer Vision, 7:11–32.

    Article  Google Scholar 

  15. Triesch J., Malsburg Ch. (2001) Democratic integration: Self-organized integration of adaptive cues, Neural Computation, 13:2049–2074

    Article  MATH  Google Scholar 

  16. Turk M. A., Pentland A. P. (1991) Face recognition using eigenfaces, In Proc. of Conf. on Comp. Vision and Patt. Rec., 586–591

    Google Scholar 

  17. Viola P., Jones M. (2001) Rapid object detection using a boosted cascade of simple features, The IEEE Conf. on Comp. Vision and Patt. Rec., 511–518

    Google Scholar 

  18. Yang M. H., Kriegman D., Ahuja N. (2002) Detecting faces in images: A survey, IEEE Trans. on Pattern Analysis and Machine Intelligence, 24:34–58

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Rzeszów University of Technology, W. Pola 2, 35-959, Rzeszów, Poland

    Bogdan Kwolek

Authors
  1. Bogdan Kwolek
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzyński , Edward Puchała , Michał Woźniak  & Andrzej żołnierek , ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kwolek, B. (2005). A Real-Time Head Tracker Supporting Human Computer Interaction. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_101

Download citation

  • DOI: https://doi.org/10.1007/3-540-32390-2_101

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25054-8

  • Online ISBN: 978-3-540-32390-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation