International Journal of Computer Vision

, Volume 10, Issue 1, pp 53–66 | Cite as

A review of statistical data association techniques for motion correspondence

  • Ingemar J. Cox


Motion correspondence is a fundamental problem in computer vision and many other disciplines. This article describes statistical data association techniques originally developed in the context of target tracking and surveillance and now beginning to be used in dynamic motion analysis by the computer vision community. The Mahalanobis distance measure is first introduced before discussing the limitations of nearest neighbor algorithms. Then, the track-splitting, joint likelihood, multiple hypothesis algorithms are described, each method solving an increasingly more complicated optimization. Real-time constraints may prohibit the application of these optimal methods. The suboptimal joint probabilistic data association algorithm is therefore described. The advantages, limitations, and relationships between the approaches are discussed.


Computer Vision Mahalanobis Distance Target Tracking Probabilistic Data Data Association 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    N. Ayache and O. Faugeras, Maintaining representations of the environment of a mobile robot,IEEE Trans. Robotics Autom. 5(6): 804–819, 1989.Google Scholar
  2. 2.
    H.A.P. Blom, R.A. Hogendoorn, and B.A. van Doorn, Design of a multisensor tracking system for advanced air traffic control. In Y. Bar-Shalom, ed.,Multitarget-Multisensor Tracking: Advanced Applications. Artech House: Norwood, MA, vol. 2, pp. 31–63, 1992.Google Scholar
  3. 3.
    Y. Bar-Shalom, K.C. Chang, and H.A.P. Blom, Automatic track formation in clutter with a recursive algorithm. In Y. Bar-Shalom, ed.,Multitarget-Multisensor Tracking: Advanced Applications, Artech House: Norwood, MA, pp. 25–42, 1990.Google Scholar
  4. 4.
    Y. Bar-Shalom, K.C. Chang, and H.A.P. Blom, Tracking splitting targets in clutter by using an interacting multiple model joint probabilistic data association filter. In Y. Bar-Shalom, ed.,Multitarget-Multisensor Tracking: Advanced Applications, Artech House: Norwood, MA, vol. 2, pp. 93–110, 1992.Google Scholar
  5. 5.
    Y. Bar-Shalom and T.E. Fortmann,Tracking and Data Association, Academic Press: San Diego, CA, 1988.Google Scholar
  6. 6.
    Y. Bar-Shalom and A.G. Jaffer, Adaptive nonlinear filtering for tracking with measurements of uncertain origin,Proc. 11th IEEE Conf. on Decision and Control, pp. 243–247, 1972.Google Scholar
  7. 7.
    Y.L. Chang and J.K. Aggarwal, 3d structure reconstruction from an ego motion sequence using statistical estimation and detection theory,IEEE Workshop in Visual Motion, pp. 268–273, 1991.Google Scholar
  8. 8.
    I.J. Cox and J.J. Leonard, Probabilistic data association for dynamic world modeling: A multiple hypothesis appraoch,Proc. Intern. Conf. Advanced Robotics, Pisa, Italy, 1991.Google Scholar
  9. 9.
    I.J. Cox and J.J. Leonard, Unsupervised learning for mobile robot navigation using probabilistic data association,Workshop on Computer Learning and Natural Learning, Berkeley, CA, 1991.Google Scholar
  10. 10.
    I.J. Cox, J.M. Rehg, and S. Hingorani, A Bayesian multiple hypothesis approach to contour segmentation,Proc. 2nd Europ. Conf. Comput. Vis., pp. 72–77, Italy, 1992.Google Scholar
  11. 11.
    J.L. Crowley, P. Stelmaszyk, and C. Discours, Measuring image flow by tracking edge-lines,Proc. Intern. Conf. Comput. Vis., pp. 658–664, Tampa, FL, 1988.Google Scholar
  12. 12.
    J.B. Collins and J.K. Uhlmann, Efficient gating in data association with multivariate Gaussian distributions,NRL, 1992.Google Scholar
  13. 13.
    M.R.W. Dawson, The how and why of what went where in apparent motion: Modeling solutions to the motion correspondence problem,Psychological Review 98(4): 569–603, 1991.Google Scholar
  14. 14.
    R. Deriche and O. Faugeras, Tracking line segments. In O. Faugeras, ed.,Proc. Europ. Conf. Comput. Vis., pp. 259–268. Springer-Verlag: New York, 1990.Google Scholar
  15. 15.
    R.O. Duda and P.E. Hart.Pattern Classification and Scene Analysis. Wiley: New York, 1973.Google Scholar
  16. 16.
    T.E. Fortmann, Y. Bar-Shalom, and M. Scheffe, Sonar tracking of multiple targets using joint probabilistic data association,IEEE J. Ocean. Engineer. OE-8(3): 173–184, 1983.Google Scholar
  17. 17.
    M.R. Garey and D.S. Johnson,Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman: New York, 1979.Google Scholar
  18. 18.
    R.W. Hockney and J.W. Eastwood,Computer Simulation Using Particles. Adam Hilger: Bristol, UK, 1988.Google Scholar
  19. 19.
    T. Kurien, Issues in the design of practical multitarget tracking algorithms. In Y. Bar-Shalom, ed.,Multitarget-Multisensor Tracking: Advanced Applications, pp. 43–83, Artech House: Norwood, MA, 1990.Google Scholar
  20. 20.
    J.E.W. Mayhew and J.P. Frisby, Psychophysical and computational studies towards a theory of human stereopsis,Artificial Intelligence, 17, 1981.Google Scholar
  21. 21.
    C.L. Morefield, Application of 0-1 interger programming to multitarget tracking problems,IEEE Trans. Autom. Contr. AC-22(6), June 1977.Google Scholar
  22. 22.
    M.J.L. Orr, J. Hallam, and R.B. Fisher, Fusion through interpretation. In G. Sandini, ed.,Second European Conference on Computer Vision, pp. 801–805. Springer-Verlag: New York, 1992.Google Scholar
  23. 23.
    F.P. Preparata and M.I. Shamos,Computational Geometry: An Introduction. Springer-Verlag: New York, 1985.Google Scholar
  24. 24.
    D.B. Reid, An algorithm for tracking multiple targets.IEEE Trans. Autom. Contr. AC-24(6): 843–854, December 1979.Google Scholar
  25. 25.
    I. Rock and S. Palmer, The legacy of gestalt psychology,Scientific American 263(6): 84–90, December 1990.Google Scholar
  26. 26.
    H.M. Salkin,Integer Programming. Addison-Wesley: Reading, MA, 1975.Google Scholar
  27. 27.
    P. Smith and G. Buechler, A branching algorithm for discriminating and tracking multiple objects,IEEE Trans. Autom. Contr. AC-20: 101–104, 1975.Google Scholar
  28. 28.
    C.W. Therrien,Decision Estimation and Classification: An Introduction to Pattern Recognition and Related Topics. Wiley: New York, 1989.Google Scholar
  29. 29.
    J.K. Uhlmann, Adaptive partitioning strategies for ternary tree structures,Patt. Recog. Ltts. 12: 537–541, 1991.Google Scholar
  30. 30.
    J.K. Uhlmann, Satisfying general proximity/similarity queries with metric trees, Inform. Process. Ltts. 40: 175–179, 1991.Google Scholar
  31. 31.
    J.K. Uhlmann, Algorithms for multiple-target tracking.American Scientist 80: 128–141, 1992.Google Scholar
  32. 32.
    Z. Zhang and O.D. Faugeras, Three-dimensional motion computation and object segmentation in a long sequence of stereo frames,Intern. J. Comput. Vis. 7(3): 211–241, 1992.Google Scholar
  33. 33.
    B. Zhou,Multitarget Tracking in Clutter: Algorithms for Data Association and State Estimation. Ph.D. thesis, Pennsylvania State University, 1992.Google Scholar

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Ingemar J. Cox
    • 1
  1. 1.NEC Research InstitutePrinceton

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