Journal of Real-Time Image Processing

, Volume 2, Issue 1, pp 45–54

Real-time predictive zoom tracking for digital still cameras

Original Research Paper


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.


Real-time zoom tracking Real-time trace curve tracking Digital still cameras Predictive models Comparison of zoom tracking approaches 


  1. 1.
    Ben-Israel, A., Greville, T.: Generalized Inverses. Springer, New York (2003)MATHGoogle Scholar
  2. 2.
    Born, M., Wolf, E.: Principle of Optics. Cambridge University Press, Cambridge (1997)Google Scholar
  3. 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)Google Scholar
  4. 4.
    Fayman, J., Sudarsky, O., Rivlin, E.: Zoom tracking. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 2783–2788 (1998)Google Scholar
  5. 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)Google Scholar
  6. 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)Google Scholar
  7. 7.
    Golub, G., Loan, C.: Matrix Computations. The John Hopkins University Press, Maryland (1996)MATHGoogle Scholar
  8. 8.
    Haber, R., Keviczky, L.: Nonlinear System Identification: Input–Output Modeling Approach. Springer, New York (1999)MATHGoogle Scholar
  9. 9.
    Hagan, M., Demuth, H., Beale, M.: Neural Network Design. PWS Publishing Company, Boston (1995)Google Scholar
  10. 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)Google Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    Kikuchi, A.: Zoom tracking apparatus. US Patent 5,212,598, Sony Corporation (1993)Google Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    Lee, S., Park, J.: Zoom tracking apparatus and method in a video camera. US patent no. 5,815,203, Goldstar Company (1998)Google Scholar
  15. 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)Google Scholar
  16. 16.
    Ljung, L.: System Identification: Theory for the User. Prentice Hall, Englewood Cliffs (1998)Google Scholar
  17. 17.
    MacKay, D.: Bayesian interpolation. Neural Comput. 4(3), 405–447 (1992)Google Scholar
  18. 18.
    Nakata, K., Oshima, S.: Zoom lens capable of being adjusted for tracking. US Patent 5,204,779, Canon Kabushiki Kaisha (1993)Google Scholar
  19. 19.
    Nelles, O.: Nonlinear System Identification. Springer, New York (2005)Google Scholar
  20. 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)Google Scholar
  21. 21.
    Ookawa, K., Jounen, A.: Auto-focus technology for video movie cameras. Mitsubishi Electric Adv. 57, 32–33 (1991)Google Scholar
  22. 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)Google Scholar
  23. 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. 24.
    Peddigari, V., Kehtarnavaz, N.: A relational approach to zoom tracking for digital still cameras. IEEE Trans. Consum. Electron. 51(4), 1051–1059 (2005)CrossRefGoogle Scholar
  25. 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)Google Scholar
  26. 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)Google Scholar
  27. 27.
    Tanaka, T.: Lens control apparatus. US Patent 6,184,932, Canon Kabhushiki Kaisha (2001)Google Scholar
  28. 28.
    TMS320DM320 System Specification. Texas Instruments Technical Reference Manual (2003)Google Scholar
  29. 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)Google Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of Texas at DallasRichardsonUSA

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