Skip to main content
SpringerLink
Log in

Real-time predictive zoom tracking for digital still cameras

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

Zoom tracking is becoming a standard feature in digital still cameras (DSCs). It involves keeping an object of interest in focus during the zooming-in or zooming-out operation. Zoom tracking is normally achieved by moving the focus motor in real-time according to the so-called trace curves in response to changes in the zoom motor position. A trace curve denotes in-focus motor positions versus zoom motor positions for a specific object distance. A zoom tracking approach is characterized by the way these trace curves are estimated and followed. In this paper, a new zoom tracking approach, named predictive zoom tracking (PZT), is introduced based on two prediction models: auto-regressive and recurrent neural network. The performance of this approach is compared with the existing zoom tracking approaches commonly used in DSCs. The real-time implementation results obtained on an actual digital camera platform indicate that the developed PZT approach not only achieves higher tracking accuracies but also effectively addresses the key challenge of zoom tracking, namely the one-to-many mapping problem.

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
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ben-Israel, A., Greville, T.: Generalized Inverses. Springer, New York (2003)

    MATH  Google Scholar 

  2. Born, M., Wolf, E.: Principle of Optics. Cambridge University Press, Cambridge (1997)

    Google Scholar 

  3. Chen, Y., Shih, S., Hung, Y., Fuh, C.: Camera calibration with a motorized zoom lens. In: Proceedings of the IEEE International Conference on Pattern Recognition, pp. 495–498 (2000)

  4. Fayman, J., Sudarsky, O., Rivlin, E.: Zoom tracking. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2783–2788 (1998)

  5. Gamadia, M., Peddigari, V., Kehtarnavaz, N., Lee, S., Cook, G.: Real-time implementation of autofocus on the TI DSC processor. In: Proceedings of the SPIE Electronic Imaging Symposium, pp. 10–18 (2004)

  6. Gamadia, M., Kehtarnavaz, N.: A real-time continuous automatic focus algorithm for digital cameras. In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 163–167 (2006)

  7. Golub, G., Loan, C.: Matrix Computations. The John Hopkins University Press, Maryland (1996)

    MATH  Google Scholar 

  8. Haber, R., Keviczky, L.: Nonlinear System Identification: Input–Output Modeling Approach. Springer, New York (1999)

    MATH  Google Scholar 

  9. Hagan, M., Demuth, H., Beale, M.: Neural Network Design. PWS Publishing Company, Boston (1995)

    Google Scholar 

  10. Hoad, P., Illingworth, J.: Automatic control of camera pan, zoom and focus for improving object recognition. In: Proceedings of 5th IEE International Conference on Image Processing and its Applications, pp. 291–295 (1995)

  11. Kehtarnavaz, N., Oh, H.: Development and real-time implementation of a rule-based auto-focus algorithm. Real Time Imaging 9(3), 197–203 (2003)

    Article  Google Scholar 

  12. Kikuchi, A.: Zoom tracking apparatus. US Patent 5,212,598, Sony Corporation (1993)

  13. Kim, Y., Lee, J., Morales, A.: A video camera system with enhanced zoom tracking and auto white balance. IEEE Trans. Consum. Electron. 48(3), 428–434 (2002)

    Article  Google Scholar 

  14. Lee, S., Park, J.: Zoom tracking apparatus and method in a video camera. US patent no. 5,815,203, Goldstar Company (1998)

  15. Lee, J., Ko, S., Kim, Y., Morales, A.: A video camera system with adaptive zoom tracking. In: International Conference on Consumer Electronics, pp. 56–57 (2002)

  16. Ljung, L.: System Identification: Theory for the User. Prentice Hall, Englewood Cliffs (1998)

    Google Scholar 

  17. MacKay, D.: Bayesian interpolation. Neural Comput. 4(3), 405–447 (1992)

    Google Scholar 

  18. Nakata, K., Oshima, S.: Zoom lens capable of being adjusted for tracking. US Patent 5,204,779, Canon Kabushiki Kaisha (1993)

  19. Nelles, O.: Nonlinear System Identification. Springer, New York (2005)

    Google Scholar 

  20. Okajima, T., Sugimoto, K.: Automatic focusing device of image pickup device and method of correcting zoom tracking curve. US Patent 6,624,851, Sanyo Electric Company (2003)

  21. Ookawa, K., Jounen, A.: Auto-focus technology for video movie cameras. Mitsubishi Electric Adv. 57, 32–33 (1991)

    Google Scholar 

  22. Peddigari, V., Kehtarnavaz, N., Lee, S.-Y., Cook, G.: Real-time implementation of zoom tracking on TI DM processor. In: Proceedings of the SPIE Electronic Imaging Symposium, pp. 8–18 (2005)

  23. Peddigari, V., Gamadia, M., Kehtarnavaz, N.: Real-time implementation issues in passive automatic focusing for digital still cameras. J. Imaging Sci. Technol. 49(2), 114–123 (2005)

    Google Scholar 

  24. Peddigari, V., Kehtarnavaz, N.: A relational approach to zoom tracking for digital still cameras. IEEE Trans. Consum. Electron. 51(4), 1051–1059 (2005)

    Article  Google Scholar 

  25. Shiokawa J., Chiba, H., Murakami, T., Todaka, Y., Ohsaka, I., Azumi, T.: Video camera with autofocus function and method of controlling the same. US Patent 5,486,860, Hitachi (1996)

  26. Shiokama, Y., Yasukawa, S.: Automatic focusing device which inhibits tracking drive control with a zoom lens having focus shift. US Patent 5,797,048, Nikon Corporation (1998)

  27. Tanaka, T.: Lens control apparatus. US Patent 6,184,932, Canon Kabhushiki Kaisha (2001)

  28. TMS320DM320 System Specification. Texas Instruments Technical Reference Manual (2003)

  29. Zheng, J., Sakai, T., Abe, N.: Guiding robot motion using zooming and focusing. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1076–1082 (1996)

Download references

Acknowledgments

This work was partially sponsored by the Imaging and Audio Business Unit of Texas Instruments.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Dallas, P.O. Box 830688, Richardson, TX, 75083-0688, USA

    V. Peddigari & N. Kehtarnavaz

Authors
  1. V. Peddigari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Kehtarnavaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Peddigari.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Peddigari, V., Kehtarnavaz, N. Real-time predictive zoom tracking for digital still cameras. J Real-Time Image Proc 2, 45–54 (2007). https://doi.org/10.1007/s11554-007-0036-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-007-0036-y

Keywords

Search

Navigation