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Motion Analysis

  • Alberto Del Bimbo
  • Simone Santini

Abstract

In this paper, we review motion analysis from monocular views under perspective transform, with reference to significant experiences. We consider two schemes for motion estimation: the Correspondence scheme and the Gradient scheme, discuss their pros and cons and their suitability for implementation in the algorithmic and connectionist computing paradigm.

Keywords

Optical Flow Motion Estimation Motion Analysis Motion Parameter Machine Intelligence 
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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References

  1. 1.
    J.Y. Lettvin, H.R. Maturana, W.S. McCulloch, and W.H. Pitts, What the frog’s eye tells the frog’s brain, Proceedings of the Institute of Radio Engineers, Vol.47, pp. 1950–1961 (1959).Google Scholar
  2. 2.
    B. MacLennan, Flexible computing in the 21st century, Vector Register, Vol.4, No.3 (1991).Google Scholar
  3. 3.
    E.I. Knudsen, S. du Lac, and S.D. Esterly, Computational maps in the brain, Annual Review of Neuroscience, Vol.10, pp. 41–65 (1987).PubMedCrossRefGoogle Scholar
  4. 4.
    J.K. Aggarwal and N. Nandhakumar, On the computation of motion from sequences of images-a review, Proceedings of the IEEE, Vol.76, No.8, pp. 917–935 (1988).CrossRefGoogle Scholar
  5. 5.
    T.S. Huang and R.Y. Tsai, Image sequence analysis: motion estimation, Springer-Verlag (1981).Google Scholar
  6. 6.
    P. Thevenaz, Motion analysis, in Pattern Recognition and Image Processing in Physics, R.A. Vaugham ed., Adam Hilger, pp. 129-166 (1990).Google Scholar
  7. 7.
    V. Cappellini, A. Del Bimbo, and A. Mecocci, Motion analysis and representation in computer vision, Journal of Circuits, Systems and Computers, Vol.3, No.3, pp. 1–35 (1993).Google Scholar
  8. 8.
    J.K. Aggarwal, L.S. Davis, and W.N. Martin, Correspondence processes in dynamical scene analysis, Proceedings of the IEEE, Vol.69, No.5 (1981).Google Scholar
  9. 9.
    J.K. Aggarwal and R. Duda, Computer analysis of moving polygonal images, IEEE Transactions on Computers, Vol.24, No. 10, pp. 966–976 (1975).CrossRefGoogle Scholar
  10. 10.
    J.W. Roach and J.K. Aggarwal, Determining the movement of objects from a sequence of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.2, No.6, pp. 554–562 (1980).Google Scholar
  11. 11.
    H.H. Nagel, Representation of moving rigid objects based on visual observations, Computer, pp. 29-39 (1991).Google Scholar
  12. 12.
    J.Q. Fang and T.S. Huang, Some experiments on estimating the 3D motion parameters of a rigid body from two consecutive image frames, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.6, No.5, pp. 545–554 (1984).PubMedCrossRefGoogle Scholar
  13. 13.
    R.Y. Tsai and T.S. Huang, Estimating three dimensional motion parameters of a rigid planar patch, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol.29, No.6, pp. 1147–1152 (1981).CrossRefGoogle Scholar
  14. 14.
    R.Y. Tsai, T.S. Huang, and W.L. Zhu, Estimating three dimensional motion parameters of a rigid planar patch II: Singular value decomposition, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol.30, No.4, pp. 525–534 (1982).CrossRefGoogle Scholar
  15. 15.
    T.J. Broida and R. Chellapa, Estimation of object motion parameters from noisy images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.8, No.1, pp. 90–99 (1986).PubMedCrossRefGoogle Scholar
  16. 16.
    H. Shariat and K.E. Price, Motion estimation with more than two frames, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.12, No.5, pp. 417–434 (1990).CrossRefGoogle Scholar
  17. 17.
    Y. Lin and T.S. Huang, Vehicle-type motion estimation from multi-frame images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No.8 (1993).Google Scholar
  18. 18.
    A. Mitchie, S. Seida, and J.K. Aggarwal, Interpretation of structure and motion from line correspondences, in Proceedings IEEE 8th International Conference on Pattern Recognition, Paris, F, Vol.2, pp. 1110–1112 (1986).Google Scholar
  19. 19.
    B. Schunck, Image flow segmentation and estimation by constraint line clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.11, No.10, pp. 1010–1027 (1989).CrossRefGoogle Scholar
  20. 20.
    O. Braddick, A short range process in apparent motion, Vision research, Vol.14 (1974).Google Scholar
  21. 21.
    B.K. Horn and B.G. Schunck, Determining optical flow, Artificial Intelligence, Vol.17, pp. 185–204 (1981).CrossRefGoogle Scholar
  22. 22.
    P. Nesi, A. Del Bimbo, and J.L.C. Sanz, Multiconstraint-based optical flow estimation and segmentation, in Proceedings of the CAMP workshop, Paris, F, pp. 419-426 (1991).Google Scholar
  23. 23.
    B.G. Schunck, The motion constraint equation for optical flow, in Proceedings of the 7th IEEE International conference on Pattern Recognition, pp. 20-22 (1984).Google Scholar
  24. 24.
    H.H. Nagel, On a constraint for the estimation of displacement rates in image seqeunces, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.11, No.1, pp. 490–498 (1989).CrossRefGoogle Scholar
  25. 25.
    A. Del Bimbo, P. Nesi, and J. L. C. Sanz, Optical flow computation using extended constraints, Technical Report DSI-5-93, Dipartimento di Sistemi e Informatica, Università di Firenze, submitted to IEEE Transactions on Image Processing (1993).Google Scholar
  26. 26.
    H.H. Nagel, Displacement vectors derived from second order intensity variations in image sequences, Computer Vision, Graphics and Image Processing, Vol.21, pp. 85–117 (1983).CrossRefGoogle Scholar
  27. 27.
    H.H. Nagel and W. Enkelmann, An investigation of smoothness constraints for the estimation of displacement vecto fields from image sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.8, No.9, pp. 565–593 (1986).PubMedCrossRefGoogle Scholar
  28. 28.
    L.S. Davis and T.R. Kushner, Road boundary detection for autonomous vehicle navigation, Technical Report CS-TR-1538, Center for automation research, Univ. of Maryland (1985).Google Scholar
  29. 29.
    E.C. Hildreth, Computing the velocity field along contours in motion: representation and perception, Elsevier Science (1986).Google Scholar
  30. 30.
    A. Mitchie, Y.F. Wang, and J.K. Aggarwal, Experiments in computing optical flow with gradient-based multiconstraint methods, Pattern Recognition, Vol.16, No.6, pp. 173–179 (1983).Google Scholar
  31. 31.
    R.M. Haralick and J.S. Lee, The facet approach to optical flow, in Proceedings of Image Understanding Workshop, Arlington ed. (1993).Google Scholar
  32. 32.
    O. Tretiak and L. Pastor, Velocity estimation from image sequence witgh second order differential operators, in Proceedings of the 7th IEEE Int. Conference on Pattern Recognition, pp. 16-19 (1984).Google Scholar
  33. 33.
    A. Verri, F. Girosi, and V. Torre, Differential techniques for optical flow, Journal of the Optical Society of America, Vol.7, No.5, pp. 912–922 (1990).CrossRefGoogle Scholar
  34. 34.
    H.H. Nagel, Extending the oriented smoothness constraint into the temporal domain and the estimation of derivatives of optical flow, in Proceedings of the 1st European Conference on Computer Vision, Nice, F (1990).Google Scholar
  35. 35.
    E. De Micheli, V. Torre, and S. Uras, The accuracy of the computation of optical flow and of the recovery of motion parameters, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15, No.5, pp. 434–447 (1993).CrossRefGoogle Scholar
  36. 36.
    G. Adiv, Determining three dimensional motion and structure from optical flowgenerated by several moving objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.7, No.5, pp. 384–401 (1985).PubMedCrossRefGoogle Scholar
  37. 37.
    G. Adiv, Inherent ambiguities in recovering 3d motion and structure from a noisy field, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.11, No.5, pp. 477–489 (1989).CrossRefGoogle Scholar
  38. 38.
    B. MacLennan, Characteristics of connectionist knowledge representation, Technical Report CS-91-147, Computer Science Department, University of Tennessee, Knoxville, TN (1991).Google Scholar
  39. 39.
    J.J. Gibson, The Perception of the Visual World, Houghton Mifflin, Boston, MA (1950).Google Scholar
  40. 40.
    P.A. Viola, S.G. Lisberger, and T.J. Sejnowski, Recurrent eye tracking network using a distributed representation of image motion, in Advances in Neural Information Processing Systems 4, J.E. Moody, S.J. Hanson, and R.P. Lippmann eds., Morgan Kaufman, San Mateo, CA (1992).Google Scholar
  41. 41.
    J.A. Marshall, Self-organizing neural network for perception of visual motion, Neural Networks, Vol.3, pp. 45–74 (1990).CrossRefGoogle Scholar
  42. 42.
    J.A. Marshall, Challenges of vision theory: self-organization of neural mechanisms for stable steering of object-grouping data in visual motion perception, in Stochastics and Neural Methods in Signal Processing, Image Processing and Computer Vision-Proceedings of the SPIE, Su-Shing Chen ed., pp. 200-215 (1991).Google Scholar
  43. 43.
    T. Delbrück and C.A. Mead, An electronic photoreceptor sensitive to small changes in intensity, in Advances in Neural Information Processing Systems 1, D.D. Touretzky ed., Morgan Kaufmann, San Mateo, CA, pp. 720–727 (1989).Google Scholar
  44. 44.
    J. Tanner and C. Mead, Optical motion sensor, in Analog VLSI and Neural Systems, C. Mead ed., Addison-Wesley, pp. 229-255 (1989).Google Scholar
  45. 45.
    J.C. Leeo, B.J. Sheu, W.C. Fang, and R. Chellappa, VLSI neuroprocessors for video motion detection, IEEE Transactions on Neural Networks, Vol.4, No.2 (1993).Google Scholar
  46. 46.
    M.I. Sereno, Learning the solution to the aperture problem for pattern motion with a hebb rule, in Advances in Neural Information Processing Systems, D.S. Touretzky ed., Morgan Kaufmann, San Mateo, CA, pp. 468–476 (1989).Google Scholar
  47. 47.
    C. Colombo, A. Del Bimbo, and S. Santini, A massively parallel architecture for the determination of optical flow, in Proceedings of the 11th International Conference of Pattern Recognition, Le Hague, NL, pp. 209-213 (1992).Google Scholar
  48. 48.
    Bruce MacLennan, Gabor representations of spatiotemporal visual images, Technical Report CS-91-144, Computer Science Department, University of Tennessee (1991).Google Scholar
  49. 49.
    W. Burger and B. Bhanu, Estimating 3D egomotion from perspective image sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.12, No.11, pp. 1040–1058 (1990).CrossRefGoogle Scholar
  50. 50.
    R. Jain, Direct computation of the focus of expansion, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.5, No.1, pp. 58–64 (1983).PubMedCrossRefGoogle Scholar
  51. 51.
    K. Rangarajan and M. Shah, Interpretation of motion trajectories using focus of expansion, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.14, No.12, pp. 1205–1209 (1992).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Alberto Del Bimbo
    • 1
  • Simone Santini
    • 1
  1. 1.Dipartimento di Sistemi e InformaticaUniversità di FirenzeFirenzeItaly

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