Skip to main content
SpringerLink
Log in

Multi-camera head pose estimation

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Estimating people’s head pose is an important problem, for which many solutions have been proposed. Most existing solutions are based on the use of a single camera and assume that the head is confined in a relatively small region of space. If we need to estimate unintrusively the head pose of persons in a large environment, however, we need to use several cameras to cover the monitored area. In this work, we propose a novel solution to the multi-camera head pose estimation problem that exploits the additional amount of information that provides multi-camera configurations. Our approach uses the probability estimates produced by multi-class support vector machines to calculate the probability distribution of the head pose. The distributions produced by the cameras are fused, resulting in a more precise estimate than the one provided individually. We report experimental results that confirm that the fused distribution provides higher accuracy than the individual classifiers and a high robustness against errors.

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

Similar content being viewed by others

References

  1. Ba S.O., Odobez J.-M.: From camera head pose to 3d global room head pose using multiple camera views. Lect. Notes Comput Sci 4625, 276–286 (2008)

    Article  Google Scholar 

  2. Basu, S., Essa, I., Pentland, A.: Motion regularization for model-based head tracking. In: International Conference on Pattern Recognition, pp. 611–616 (1996)

  3. Bishop, C.M.: Pattern recognition and machine learning. Springer, New York (2006)

  4. Black, M., Yacoob, Y.: Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion. In: International Conference on Computer Vision, pp. 374–381 (1995)

  5. Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly, Sebastopol, CA (2008)

  6. Canton-Ferrer C., Segura C., Casas J.R., Pardas M., Hernando J.: Audiovisual head orientation estimation with particle filtering in multisensor scenarios. EURASIP J. Adv. Signal Process. 4625, 1–12 (2008)

    Google Scholar 

  7. Cascia M.L., Sclaroff S., Athitsos V.: Fast, reliable head tracking under varying illumination: an approach based on registration of textured-mapped 3d models. Pattern Anal. Mach. Intell. 22, 322–336 (2000)

    Article  Google Scholar 

  8. Chen, L., Zhang, L., Hu, Y., Li, M., Zhang, H.: Head pose estimation using fisher manifold learning. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 203–207 (2003)

  9. Choi K.N., Worthington P.L., Hancock E.R.: Estimating facial pose using shape-from-shading. Pattern Recogn. Lett. 23, 533–548 (2002)

    Article  MATH  Google Scholar 

  10. Choia S., Kim D.: Robust head tracking using 3d ellipsoidal head model in particle filter. Pattern Recognit. 41, 2901–2915 (2008)

    Article  Google Scholar 

  11. Cootes T.F., Wheeler G.V., Walker K.N., Taylor C.J.: View-based active appearance models. Image Vis. Comput. 20, 657–664 (2002)

    Article  Google Scholar 

  12. Fukunaga K.: Introduction to Statistical Pattern Recognition. Academic Press, New York (1990)

    MATH  Google Scholar 

  13. Gee A., Cipolla R.: Fast visual tracking by temporal consensus. Image Vis. Comput. 14, 105–114 (1996)

    Article  Google Scholar 

  14. Weng R.C., Lin H.T., Lin C.J.: A note on platt’s probabilistic outputs for support vector machines. Mach. Learn. 68, 267–276 (2007)

    Article  Google Scholar 

  15. Horprasert, T., Yacoob, Y., Davis, L.S.: Computing 3-D orientation from a monocular image sequence. In: IEEE Conference on automatic face and gesture recognition, pp. 242–247 (1996)

  16. Huang, J., Shao, X., Wechsler, H.: Face pose estimation using support vector machines. In: International Conference on pattern recognition, pp. 154–156 (1998)

  17. Ji Q.: 3D face pose estimation and tracking from a monocular camera. Image Vis. Comput. 20, 499–511 (2002)

    Article  Google Scholar 

  18. Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Neurocomputing: Algorithms, Architectures and Applications. NATO ASI Series. Springer, Heidelberg (1990)

  19. Krüger V., Sommer G.: Gabor wavelet networks for efficient head posenext term estimation. Image Vis. Comput. 20, 665–672 (2002)

    Article  Google Scholar 

  20. Lanz O., Brunelli R.: Joint bayesian tracking of head location and pose from low-resolution video. Lect. Notes Comput. Sci. 4625, 287–296 (2008)

    Article  Google Scholar 

  21. Lee M.W., Ranganath S.: Pose-invariant face recognition using a 3D deformable model. Pattern Recognit. 36, 1835–1846 (2003)

    Article  Google Scholar 

  22. Li, Y., Gong, S., Liddell, H.: Support vector regression and classification based multi-view face detection and recognition. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 300–305 (2000)

  23. Lin C., Fan K.-C.: Pose classification of human faces by weighting mask function approach. Pattern Recognit. Lett. 24, 1857–1869 (2003)

    Article  Google Scholar 

  24. Malassiotis S., Strintzis M.G.: Robust real-time 3d head posenext term estimation from range data. Pattern Recognit. 38, 1153–1165 (2005)

    Article  Google Scholar 

  25. Muñoz Salinas R.: A bayesian plan-view map based approach for multiple-person detection and tracking. Pattern Recognit. 41, 3665–3676 (2008)

    Article  MATH  Google Scholar 

  26. Muñoz Salinas R., García-Silvente M., Medina-Carnicer R.: Adaptive multi-modal stereo people tracking without background modelling. J Vis Commun Image Represent 19, 75–91 (2008)

    Article  Google Scholar 

  27. Muñoz Salinas R., Medina-Carnicer R., Madrid-Cuevas F.J., Carmona-Poyato A.: Multi-camera people tracking using evidential filters. Int J Approx Reason 50, 732–749 (2009)

    Article  Google Scholar 

  28. Muñoz Salinas R., Medina-Carnicer R., Madrid-Cuevas F.J., Carmona-Poyato A.: People detection and tracking with multiple stereo cameras using particle filters. J Vis Commun Image Represent 20, 339–350 (2009)

    Article  Google Scholar 

  29. Rajwade A., Levine M.D.: Facial pose from 3d data. Image. Vis. Comput. 24, 849–856 (2006)

    Article  Google Scholar 

  30. Saffiotti, A., Broxvall, M.: PEIS ecologies: Ambient intelligence meets autonomous robotics. In: Proceedings of the International Conference on Smart Objects and Ambient Intelligence (sOc-EUSAI), pp. 275–280, Grenoble (2005)

  31. Sherrah J., Gong S.: Fusion of perceptual cues for robust tracking of head posenext term and position. Pattern Recognit. 34, 1565–1572 (2001)

    Article  MATH  Google Scholar 

  32. Srinivasan, S., Boyer, K.L.: Head pose estimation using view based eigenspaces. In: 16th International Conference on Pattern Recognition, pp. 302–305 (2002)

  33. Zhang, H.J., Cheng, Q.S., Li, S.Z., Peng, X.H.: Multi-view face pose estimation based on supervised isa learning. In: IEEE International Conference Automatic Face and Gesture Recognition, pp. 100–105 (2002)

  34. Weng R.C., Wu T., Lin C.: Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5, 975–1005 (2004)

    MathSciNet  MATH  Google Scholar 

  35. Vapnik V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  36. Voit, M., Nickel, K., Stiefelhagen, R.: Multi-view head pose estimation using neural networks. In: 2nd Canadian Conference on Computer and Robot Vision, pp. 347–352 (2005)

  37. Voit M., Nickel K., Stiefelhagen R.: Head pose estimation in single- and multi-view environments—results on the clear’07 benchmarks. Lect. Notes Comput. Sci. 4625, 307–316 (2009)

    Article  Google Scholar 

  38. Voit M., Stiefelhagen R.: A system for probabilistic joint 3d head tracking and pose estimation in low-resolution, multi-view environments. Lect. Notes Comput. Sci. 5815, 415–424 (2009)

    Article  Google Scholar 

  39. Wang J.-G., Sung E.: Pose determination of human faces by using vanishing points. Pattern Recognit. 34, 2427–2445 (2001)

    Article  MATH  Google Scholar 

  40. Wanga J.-G., Sungb E.: Em enhancement of 3d head posen estimated by point at infinity. Image Vis. Comput. 25, 1864–1874 (2007)

    Article  Google Scholar 

  41. Wu J., Trivedia M.M.: A two-stage head pose estimation framework and evaluation. Pattern Recognit. 41, 1138–1158 (2008)

    Article  MATH  Google Scholar 

  42. Fradet L., Wei, Y., Tan, T.: Head pose estimation using gabor eigenspace modeling. In: IEEE International Conference on image processing, pp. 281–284 (2002)

  43. Zhang, Z., Hu, Y., Liu, M., Huang, T.: Head pose estimation in seminar room using multi view face detectors. In: CLEAR Evaluation and Workshop, pp. 281–290 (2006)

Download references

Author information

Authors and Affiliations

  1. Department of Computing and Numerical Analysis, University of Córdoba, 14071, Córdoba, Spain

    Rafael Muñoz-Salinas, E. Yeguas-Bolivar & R. Medina-Carnicer

  2. AASS Mobile Robotics Laboratory, University of Örebro, 70182, Örebro, Sweden

    A. Saffiotti

Authors
  1. Rafael Muñoz-Salinas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. Yeguas-Bolivar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Saffiotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. Medina-Carnicer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Muñoz-Salinas.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Muñoz-Salinas, R., Yeguas-Bolivar, E., Saffiotti, A. et al. Multi-camera head pose estimation. Machine Vision and Applications 23, 479–490 (2012). https://doi.org/10.1007/s00138-012-0410-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-012-0410-z

Keywords

Search

Navigation