Machine Vision and Applications

, Volume 19, Issue 1, pp 1–25 | Cite as

Real-time edge-enhanced dynamic correlation and predictive open-loop car-following control for robust tracking

  • Javed AhmedEmail author
  • M. N. Jafri
  • Mubarak Shah
  • Muhammad Akbar
Original Paper


We present a robust framework for a real-time visual tracking system, based on a BPNN-controlled fast normalized correlation (BCFNC) algorithm and a predictive open-loop car-following control (POL-CFC) strategy. The search for the target is carried out in a dynamically generated resizable search-window. In order to achieve the robustness, we use some edge-enhancement operations before the correlation operation, and introduce an adaptive template-updating scheme. The proposed tracking algorithm is compared with various correlation-based techniques and (in some cases) with the mean-shift and the condensation trackers on real-world scenarios. A significant improvement in efficiency and robustness is reported. The POL-CFC algorithm approximates the current velocity of an open-loop pan-tilt unit, computes the predicted relative-velocity of the object using Kalman filter, and generates the precise control signals to move the camera accurately towards the maneuvering target regardless of its changing velocity. The proposed system works in real-time at the speed of 25–200 frames/ second depending on the template size, and it can persistently track a distant or near object even in the presence of object fading, low-contrast imagery, noise, short-lived background clutter, object-scaling, changing object-velocity, varying illumination, object maneuvering, multiple objects, obscuration, and sudden occlusion.


Visual tracking BPNN-controlled fast normalized correlation Dynamic search-window Robust template-updating Predictive open-loop car-following control 


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  1. 1.
    Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral Histogram. In: IEEE conference on computer vision and pattern recognition (2006)Google Scholar
  2. 2.
    Ahmed, J., Jafri, M.N., Ahmad, J.: Target tracking in an image sequence using wavelet features and a neural network. In: Proceedings of IEEE Region 10: Tencon’05 Conference, Melbourne (2005)Google Scholar
  3. 3.
    Ahmed, J., Jafri, M.N., Ahmad, J., Khan, M.I.: Design and implementation of a neural network for real-time object tracking. In: Proceedings of machine vision and pattern recognition in 4th world enformatika conference, Istanbul (2005)Google Scholar
  4. 4.
    Oppenheim A.V., Schafer R.W. and Buck J.R. (1999). Discrete-Time Signal Processing. Prentice Hall, Englewood cliffs Google Scholar
  5. 5.
    Kuo, B.C.: Automatic Control Systems, 7th edn. Wiley (1995)Google Scholar
  6. 6.
    Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems, Artech House, Boston, pp. 309–313 (1999)Google Scholar
  7. 7.
    Bradski, G.R.: Computer vision face tracking as a component of a perceptual user Interface. In: IEEE Workshop on Applic. Comp. Vis., Princeton, pp. 214–219 (1998)Google Scholar
  8. 8.
    Brookner E. (1998). Tracking and Kalman Filtering Made Easy. Wiley, NewYork Google Scholar
  9. 9.
    Brunson R.L., Boesen D.L., Crockett G.A. and Riker J.F. (1992). Precision trackpoint control via correlation track referenced to simulated imagery. Society of Photo-Optical Instrumentation Engineers, Bellingham Google Scholar
  10. 10.
    Chen, Q.-s., Defrise, M., Deconinck, F.: Symmetric phase-only matched filtering of Fourier–Mellin transforms for image registration and recognition. IEEE Trans. Pattern Anal. Mach Intell. 16 (1994)Google Scholar
  11. 11.
    Comaniciu D., Visvanathan R. and Meer P. (2003). Kernel based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 564–575 CrossRefGoogle Scholar
  12. 12.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings, IEEE conference on computer vision and pattern recognition, Hilton Head, vol. 1, pp. 142–149 (2000)Google Scholar
  13. 13.
    Crow F. (1984). Summed-area tables for texture mapping. Comput. Graph. 18(3): 207–212 CrossRefGoogle Scholar
  14. 14.
    Cuevas, E.V., Zaldivar, D., Rojas, R.: Intelligent tracking. Technical Report (2003)Google Scholar
  15. 15.
    Cui, Y., Samarasekera, S., Huang, Q., Greienhagen, M.: Indoor monitoring via the collaboration between a peripheral sensor and a foveal sensor. In: IEEE Workshop on Visual Surveillance, Bombay, pp. 2–9 (1998)Google Scholar
  16. 16.
    Demuth, H., Beale, M.: Neural Network Toolbox for Use with MATLAB: User’s Guide (v. 4), The Mathworks, Inc. (2001)Google Scholar
  17. 17.
    Doulamis, A., Doulamis, N., Ntalianis, K., Kollias, S.: An efficient fully unsupervised video object segmentation scheme using an adaptive neural-network classifier archtecture. IEEE Trans. Neural Netw. (2003)Google Scholar
  18. 18.
    Eleftheriadis A. and Jacquin A. (1995). Automatic face location detection and tracking for model-assisted coding of video teleconference sequences at low bit rates. Signal Process. Image Commun. 7(3): 231–248 CrossRefGoogle Scholar
  19. 19.
    Fagiani, C., Gips, J.: An evaluation of tracking methods for human-computer interaction, Senior Thesis, Computer Science Department, Boston College, Fulton Hall, Chestnut Hill, 02467, 2002Google Scholar
  20. 20.
    Fausett, L.: Fundamentals of Neural Networks: Architectures. Algorithms, and Applications, Prentice Hall, Englewood Cliffs (1994)Google Scholar
  21. 21.
    Fitts, J.M.: Precision correlation tracking via optimal weighting functions. In: 18th IEEE conference on decision and control including the symposium on adaptive processes (1979)Google Scholar
  22. 22.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digtal Image Processing Using MATLAB, Pearson Education Pte. Ltd., Singapore (2004)Google Scholar
  23. 23.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn, Prentice-Hall, Inc., Englewoodcliffs (2002)Google Scholar
  24. 24.
    Haykin S. (1999). Neural Networks: A Comprehensive Foundation, 2nd edn. Pearson Education, Delhi Google Scholar
  25. 25.
    Isard M. and Blake A. (1998). CONDENSATION-conditional density propagation for visual tracking. Int. J. Comput. Vision 29(1): 5–28 CrossRefGoogle Scholar
  26. 26.
    Kass M., Witkin A. and Terzopoulos D. (1988). Snakes: active contour models. Int. J. Comput. Vis. 1(4): 321–331 CrossRefGoogle Scholar
  27. 27.
    Kuglin, C., Hines, D.: The Phase Correlation Image Alignment Method. In: Proceedings of International conference cybernetics and society, pp. 163–165 (1975)Google Scholar
  28. 28.
    Lewis, J.P.: Fast Normalized Cross-Correlation. Industrial Light& Magic (1995)Google Scholar
  29. 29.
    Grewal M.S. and Andrews A.P. (2001). Kalman Filtering: Theory and Practice Using MATLAB, 2nd edn. J. Wiley, New York Google Scholar
  30. 30.
    Moller M.F. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6: 525–533 CrossRefGoogle Scholar
  31. 31.
    Hayes M.H. (1999). Digital Signal Processing. McGraw-Hill, New York Google Scholar
  32. 32.
    Mir-Nasiri, N.: Camera-based 3D tracking. In: Proceedings of IEEE Region 10: Tencon’05 Conference, Melbourne (2005)Google Scholar
  33. 33.
    Nummiaroa K., Koller-Meierb E., Gool L.V. An adaptive color-based particle filter Image Vision Comput. 21, 99–110 (2003)Google Scholar
  34. 34.
    Perez, P., et al.: Color-based probabilistic tracking. European Conference on Computer Vision, pp. 661–675 (2002)Google Scholar
  35. 35.
    Porikli, F., Tuzel, O.: Multi kernel object tracking. In: Proceedings of IEEE International conference on multimedia and Expo, Amsterdam (2005)Google Scholar
  36. 36.
    Porikli, F.: Integral histogram: a fast way to extract histograms in cartesian spaces. In: IEEE conference on computer vision and pattern recognition (2005)Google Scholar
  37. 37.
    Porikli, F., Tuzel, O., Meer, P.: Covariance tracking using model update based on lie algebra. In: IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  38. 38.
    Ritter G.X. and Wilson J.N. (1996). Handbook of Computer Vision Algorithms in Image Algebra. CRC Press, Boca Raton zbMATHGoogle Scholar
  39. 39.
    Rosales, R., Sclaro, S.: 3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions. In: IEEE Conf. Comp. Vis. And Pat. Rec., vol. 2, pp. 117–123, Fort Collins (1999)Google Scholar
  40. 40.
    Intille, S.S., Davis, J.W., Bobick, A.F.: Real-time closed-world tracking, In: IEEE Conference on Comp. Vis. and Pat. Rec., Puerto Rico, pp. 697–703 (1997)Google Scholar
  41. 41.
    Stauffer C. and Grimson W. (2000). Learning patterns of activity using real time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8): 747–767 CrossRefGoogle Scholar
  42. 42.
    Stone, H.S., Tao, B., McGuire, M.: Analysis of image registration noise due to rotationally dependent aliasing. NEC Research (2000)Google Scholar
  43. 43.
    Stone, H.S.: Fourier-based image registration techniques. NEC Research (2002)Google Scholar
  44. 44.
    Umbaugh S.E. (2005). Computer Imaging: Digital Image Analysis and Processing. CRC Press, Boca Raton zbMATHGoogle Scholar
  45. 45.
    Wang, H., Suter, D., Schindler, K.: Effective appearance model and similarity measure for particle filtering and visual tracking. European Conference on Computer Vision (2006)Google Scholar
  46. 46.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter. TR 95-041, Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill 27599–3175 (2004)Google Scholar
  47. 47.
    Wong, S.: Advanced correlation tracking of objects in cluttered imagery. In: Proceedings of SPIE, vol. 5810 (2005)Google Scholar
  48. 48.
    Wren C., Azarbayejani A., Darrell T. and Pentland A. (1997). PFinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19: 780–785 CrossRefGoogle Scholar
  49. 49.
    Yilmaz A., Li X. and Shah M. (2004). Contour-based object tracking with occlusion handling in video acquired using mobile cameras. IEEE Trans. Pattern Anal. Mach. Intelli. 26(11): 1531–1536 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Javed Ahmed
    • 1
    • 2
    Email author
  • M. N. Jafri
    • 3
  • Mubarak Shah
    • 4
  • Muhammad Akbar
    • 5
  1. 1.Computer Vision LabUniversity of Central FloridaOrlandoUSA
  2. 2.Department of Electrical (Telecom.) EngineeringMilitary College of SignalsRawalpindiPakistan
  3. 3.Electrical (Telecom.) Engineering DepartmentNational University of Sciences & TechnologyRawalpindiPakistan
  4. 4.School of Electrical Engineering & Computer ScienceUniversity of Central FloridaOrlandoUSA
  5. 5.Engineering DivisionNational University of Sciences & TechnologyRawalpindiPakistan

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