Skip to main content
SpringerLink
Log in

Mutual Information-Based 3D Object Tracking

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

We propose a robust methodology for 3D model-based markerless tracking of textured objects in monocular image sequences. The technique is based on mutual information maximization, a widely known criterion for multi-modal image registration, and employs an efficient multiresolution strategy in order to achieve robustness while keeping fast computational time, thus achieving near real-time performance for visual tracking of complex textured surfaces.

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.

Similar content being viewed by others

References

  • Baker, S., & Matthews, I. (2004). Lucas–Kanade 20 years on: a unifying framework. International Journal of Computer Vision, 56(3), 221–255.

    Article  Google Scholar 

  • Black, M. J., & Jepson, A. D. (1996). Eigentracking: robust matching and tracking of articulated objects using a view-based representation. In European conference on computer vision (Vol. 1, pp. 329–342).

  • Brunelli, R., & Poggio, T. (1993). Face recognition: features versus templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10), 1042–1052.

    Article  Google Scholar 

  • Cascia, M., Sclaroff, S., & Athitsos, V. (1999). Fast, reliable head tracking under varying illumination: an approach based on registration of texture-mapped 3d models.

  • Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998). Active appearance models. Lecture Notes in Computer Science, 1407, 484–498.

    Article  Google Scholar 

  • Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: Wiley.

    MATH  Google Scholar 

  • Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis. New York: Wiley.

    MATH  Google Scholar 

  • Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395.

    Article  MathSciNet  Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading: Addison–Wesley.

    MATH  Google Scholar 

  • Gonzalez, R. C., & Woods, R. E. (2006). Digital image processing (3rd ed.). Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Gorodnichy, D., Malik, S., & Roth, G. (2002). Affordable 3d face tracking using projective vision. In International conference on vision interfaces (pp. 383–390).

  • Hager, G. D., & Belhumeur, P. N. (1998). Efficient region tracking with parametric models of geometry and illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(10), 1025–1039.

    Article  Google Scholar 

  • Huber, P. (1981). Robust statistics. New York: Wiley.

    MATH  Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942–1948).

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • Lu, L., Dai, X.-T., & Hager, G. (2004). A particle filter without dynamics for robust 3d face tracking. In Proceedings of the 2004 conference on computer vision and pattern recognition workshop (CVPRW’04) (Vol. 5, p. 70). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., & Suetens, P. (1997). Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 16(2), 187–198.

    Article  Google Scholar 

  • Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. j-J-SIAM, 11(2), 431–441.

    MATH  MathSciNet  Google Scholar 

  • Matthews, I., & Baker, S. (2003). Active appearance models revisited (Technical Report CMU-RI-TR-03-02). Robotics Institute, Carnegie Mellon University.

  • Nelder, J., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308–313.

    MATH  Google Scholar 

  • Park, I. K., Zhang, H., Vezhnevets, V., & Choh, H.-K. (2004). Image-based photorealistic 3-d face modeling. In International conference on automatic face and gesture recognition (pp. 49–56).

  • Pluim, J. P. W., Maintz, J. B. A., & Viergever, M. A. (2003). Mutual-information-based registration of medical images: a survey. IEEE Transactions on Medical Imaging, 22(8), 986–1004.

    Article  Google Scholar 

  • Principe, J., Xu, D., & Fisher, J. (1999). Information theoretic learning. In S. Haykin (Ed.), Unsupervised adaptive filtering. New York: Wiley.

    Google Scholar 

  • Shi, J., & Tomasi, C. (1994). Good features to track. In IEEE conference on computer vision and pattern recognition (CVPR’94), Seattle, June 1994.

  • Skrypnyk, I., & Lowe, D. G. (2004). Scene modelling, recognition and tracking with invariant image features. In ISMAR ’04: proceedings of the third IEEE and ACM international symposium on mixed and augmented reality (ISMAR’04) (pp. 110–119), Washington, DC, USA. Los Alamitos: IEEE Computer Society.

    Chapter  Google Scholar 

  • Thevenaz, P., & Unser, M. (2000). Optimization of mutual information for multiresolution image registration. IEEE Transactions on Image Processing, 9(12), 2083–2099.

    Article  MATH  Google Scholar 

  • Toyama, K. (1998). Look, ma—no hands!’ hands-free cursor control with real-time 3d face tracking. In Proceedings of the workshop on perceptual using interfaces (PUI’98) (pp. 49–54), San Francisco.

  • Toyama, K., & Hager, G. (1996). Incremental focus of attention for robust visual tracking. International Journal on Computer Vision, 35(1), 45–63.

    Article  Google Scholar 

  • Unser, M. (1999). Splines: a perfect fit for signal and image processing. IEEE Signal Processing Magazine, 16(6), 22–38. IEEE Signal Processing Society’s 2000 magazine award.

    Article  Google Scholar 

  • Unser, M., Aldroubi, A., & Eden, M. (1993). B-spline signal processing: part I: theory. IEEE Transactions on Signal Processing, 41(2), 821–833.

    Article  MATH  Google Scholar 

  • Unser, M., Aldroubi, A., & Eden, M. (1993). B-spline signal processing, part II: efficient design and applications. IEEE Transactions on Signal Processing, 41(2), 834–848.

    Article  MATH  Google Scholar 

  • Unser, M., Aldroubi, A., & Eden, M. (1993). The L 2-polynomial spline pyramid. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(4), 364–379.

    Article  Google Scholar 

  • Vacchetti, L., & Lepetit, V. (2004). Stable real-time 3d tracking using online and offline information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(10), 1385–1391.

    Article  Google Scholar 

  • Viola, P. A., & Jones, M. J. (2001). Robust real-time face detection. In International conference on computer vision (p. 747).

  • Wells, W., Viola, P., Atsumi, H., Nakajima, S., & Kikinis, R. (1996). Multi-modal volume registration by maximization of mutual information.

  • Xiao, J., Baker, S., Matthews, I., & Kanade, T. (2004). Real-time combined 2d + 3d active appearance models. In CVPR (pp. 535–542).

Download references

Author information

Authors and Affiliations

  1. Chair for Robotics and Embedded Systems, Technical University of Munich, Boltzmannstrasse 3, 85748, Garching bei Muenchen, Germany

    Giorgio Panin & Alois Knoll

Authors
  1. Giorgio Panin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alois Knoll
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giorgio Panin.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Cite this article

Panin, G., Knoll, A. Mutual Information-Based 3D Object Tracking. Int J Comput Vis 78, 107–118 (2008). https://doi.org/10.1007/s11263-007-0083-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-007-0083-7

Keywords

Search

Navigation