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Performance of optical flow techniques

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Abstract

While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential, matching, energy-based, and phase-based methods. Our comparisons are primarily empirical, and concentrate on the accuracy, reliability, and density of the velocity measurements; they show that performance can differ significantly among the techniques we implemented.

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References

  • Adelson, E.H., and Bergen, J.R. 1985. Spatiotemporal energy models for the perception of motion,J. Opt. Soc. Amer. A 2: 284–299.

    Google Scholar 

  • Adelson, E.H., and Bergen, J.R. 1986. The extraction of spatiotemporal energy in human and machine vision,Proc. IEEE Workshop on Visual Motion, Charleston, pp. 151–156.

  • Aloimonos, J., and Brown, C.M. 1984. Direct processing of curvilinear sensor motion from a sequence of perspective images.Proc. 2nd Workshop on Computer Vision, Annapolis, pp. 72–77.

  • Aloimonos, Y., and Duric, Z. 1992. Active egomotion estimation: a qualitative approach,Proc. Europ. Conf. Comput. Vis., Ligure, Italy, pp. 497–510.

  • Anandan, P. 1987. Measuring Visual Motion from Image Sequences. Ph.D. dissertation, COINS TR 87-21, Univ. of Massachusetts, Amherst, MA.

    Google Scholar 

  • Anandan, P. 1989. A computational framework and an algorithm for the measurement of visual motion,Intern. J. Comput. Vis. 2: 283–310.

    Google Scholar 

  • Barman, H., Haglund, L., Knutsson, H., and Granlund, G. 1991. Estimation of velocity, acceleration and disparity in time sequences,Proc. IEEE Workshop on Visual Motion, Princeton, pp. 44–51.

  • Barron, J.L., Fleet, D.J., and Beauchemin, S.S. 1993. Performance of optical flow techniques, Tech. Rept. TR299, Dept. of Computer Science, University of Western Ontario; and RPL-TR-9107, Dept. of Computing Science, Queens University, July 1992 (revised July 1993).

  • Barron, J.L., Fleet, D.J., Beauchemin, SS., and Burkitt, T. 1992. Performance of optical flow techniques,Proc. Conf. Comp. Vis. Patt. Recog., Champaign, June, pp. 236–242.

  • Barron, J.L., Jepson, A.D., and Tsotsos, J.K. 1990. The feasibility of motion and structure from noisy time-varying image velocity information,Intern. J. Comput. Vis. 5: 239–269.

    Google Scholar 

  • Beaudet, P.R. 1978. Rotationally invariant image operators.Proc. 4th Intern. Conf. Patt. Recog., Tokyo, pp. 579–583.

  • Bigun, J., Granlund, G., and Wiklund, J. 1991. Multidimensional orientation estimation with applications to texture analysis and optical flow.IEEE Trans. Patt. Anal. Mach. Intell. 13: 775–790.

    Google Scholar 

  • Burt, P.J., and Adelson, E.H. 1983. The Laplacian pyramid as a compact image code,IEEE Trans. Communications 31, pp. 532–540.

    Google Scholar 

  • Burt, P.J., Yen, C., and Xu, X. 1983. Multiresolution flow-through motion analysis,Proc. Conf. Comput. Vis. Patt. Recog., Washington, pp. 246–252.

  • Buxton, B., and Buxton, H. 1984. Computation of optic flow from the motion of edge features in image sequences,Image Vis. Comput. 2: 59–74.

    Google Scholar 

  • Cippola, R., and Blake, A. 1992. Surface orientation and time to contact from image divergence and deformation,Proc. 2nd Europ. Conf. Comput. Vis., Ligure, Italy, pp. 187–202.

  • Duncan, J.H., and Chou, T.C. 1988. Temporal edges: The detection of motion and the computation of optical flow,Proc. 2nd Intern. Conf. Comput. Vis., Tampa, pp. 374–382.

  • Dutta, R., Manmatha, R., Williams, L., and Riseman, E.M. 1989. A data set for quantitative motion analysis,Proc. Conf. Comput. Vis. Patt. Recog., San Diego, pp. 159–164.

  • Fennema, C., and Thompson, W. 1979. Velocity determination in scenes containing several moving objects,Comput. Graph. Image Process. 9: 301–315.

    Google Scholar 

  • Fleet, D.J. 1992.Measurement of Image Velocity. Kluwer Academic Publishers: Norwell, MA.

    Google Scholar 

  • Fleet, D.J., and Jepson, A.D. 1990. Computation of component image velocity from local phase information,Intern. J. Comput. Vis. 5: 77–104.

    Google Scholar 

  • Fleet, D.J., and Jepson, A.D. 1993. Stability of phase information,IEEE Trans. Patt. Anal. Mach. Intell. (in press).

  • Fleet, D.J., and Langley, K. 1993. Toward real-time optical flow,Proc. Vision Interface, Toronto, pp. 116–124 (also see Tech. Rept.: RPL-TR-9308, Robotics and Perception Laboratory, Queen's University).

  • Girosi, F., Verri, A., and Torre, V. 1989. Constraints for the computation of optical flow,Proc. IEEE Workshop on Visual Motion, Irvine, pp. 116–124.

  • Glazer, F., Reynolds, G., and Anandan, P. 1983. Scene matching through hierarchical correlation,Proc. Conf. Comput. Vis. Patt. Recog., Washington, pp. 432–441.

  • Grzywacz, N.M., and Yuille, A.L. 1990. A model for the estimation of local image velocity by cells in the visual cortex,Proc. Roy. Soc. London B 239: 129–161.

    Google Scholar 

  • Haglund, L. 1992. Adaptive Multidimensional Filtering. Ph.D. dissertation, Dept. Electrical Engineering, Univ. of Linkoping (ISSN 0345-7524).

  • Heeger, D.J. 1987. Model for the extraction of image flow,J. Opt. Soc. Amer. A 4: 1455–1471.

    Google Scholar 

  • Heeger, D.J. 1988. Optical flow using spatiotemporal filters,Intern. J. Comput. Vis. 1: 279–302.

    Google Scholar 

  • Hildreth, E.C. 1984. The computation of the velocity field,Proc. Roy. Soc. London B 221: 189–220.

    Google Scholar 

  • Horn, B.K.P. 1986.Robot Vision. MIT Press: Cambridge, MA.

    Google Scholar 

  • Horn, B.K.P., and Schunck, B.G. 1981. Determining optical flow,Artificial Intelligence 17: 185–204.

    Google Scholar 

  • Horn, B.K.P., and Weldon, Jr., E.J. 1988. Direct methods for recovering motion,Intern. J. Comput. Vis. 2: 51–76.

    Google Scholar 

  • Jahne, B. 1987. Image sequence analysis of complex physical objects: nonlinear small scale water surface waves,Proc. 1st Intern. Conf. Comput. Vis., London, pp. 191–200.

  • Jepson, A.D., and Fleet, D.J. 1991. Phase singularities in scale-space,Image Vis. Comput. 9: 338–343.

    Google Scholar 

  • Jepson, A.D., and Heeger, D.J. 1990. Subspace methods for recovering rigid motion II: Theory, Tech. Rept. RBCV-TR-90-36, Dept. of Computer Science, University of Western Ontario. (To appear inInt. J. Comput. Vis.).

  • Kearney, J.K., Thompson, W.B., and Boley, D.L. 1987. Optical flow estimation: An error analysis of gradient-based methods with local optimization,IEEE Trans. Patt. Anal. Artif. Mach. Intell. 9:229–244.

    Google Scholar 

  • Little, J.J., and Verri, A. 1989. Analysis of differential and matching methods for optical flow,IEEE Workshop on Visual Motion, Irvine, CA, pp. 173–180.

  • Little, J.J., Bulthoff, H.H., and Poggio, T.A. 1988. Parallel optical flow using local voting.Proc. 2nd Intern. Conf. Comput. Vis., Tampa, pp. 454–459.

  • Lucas, B.D. 1984. Generalized Image Matching by the Method of Differences. Ph.D. dissertation, Dept. of Computer Science, Carnegie-Mellon University.

  • Lucas, B., and Kanade, T. 1981. An iterative image registration technique with an application to stereo vision.Proc. DARPA Image Understanding Workshop, pp. 121–130.

  • Marr, D., and Hildreth, E.C. 1980. Theory of edge detection,Proc. Roy. Soc. London, B 207: 187–217.

    Google Scholar 

  • Nagel, H.H. 1983. Displacement vectors derived from second-order intensity variations in image sequences,Comput. Graph. Image Process. 21: 85–117.

    Google Scholar 

  • Nagel, H.-H. 1987. On the estimation of optical flow: Relations between different approaches and some new results,Artificial Intelligence 33: 299–324.

    Google Scholar 

  • Nagel, H.-H. 1989. On a constraint equation for the estimation of displacement rates in image sequences,IEEE Trans. Patt. Anal. Mach. Intell. 11: 13–30.

    Google Scholar 

  • Nagel, H.H., and Enkelmann, W. 1986. An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences.IEEE Trans. Patt. Anal. Mach. Intell. 8: 565–593.

    Google Scholar 

  • Negahdaripour, S., and Horn, B.K.P. 1987. Direct passive navigation,IEEE Trans. Patt. Anal. Mach. Intell. 9: 168–176.

    Google Scholar 

  • Santen, J.P.H. van, and Sperling, G. 1985. Elaborated Reichardt detectors,J. Opt. Soc. Amer. A 2: 300–321.

    Google Scholar 

  • Schunck, B.G. 1984. The motion constraint equation for optical flow,Proc. 7th Intern. Conf. Patt. Recog., Montreal, pp. 20–22.

  • Schunck, B.G. 1986. Image flow continuity equations for motion and density,Proc. IEEE Workshop on Visual Motion, Charleston, pp. 89–94.

  • Simoncelli, E.P. 1993. Distributed Representation and Analysis of Visual Motion. Ph.D. dissertation, Dept. of Electrical Engineering and Computer Science, MIT.

  • Simoncelli, E.P., Adelson, E.H., and Heeger, D.J. 1991. Probability distributions of optical flow.Proc. Conf. Comput. Vis. Patt. Recog., Maui, pp. 310–315.

  • Singh, A. 1990. An estimation-theoretic framework for image-flow computation,Proc. 3rd Intern. Conf. Comput. Vis., Osaka, pp. 168–177.

  • Singh, A. 1992.Optic Flow Computation: A Unified Perspective. IEEE Computer Society Press.

  • Tistarelli, M., and Sandini, G. 1990. Estimation of depth from motion using anthropomorphic visual sensor,Image Vis. Comput. 8: 271–278.

    Google Scholar 

  • Tretiak, O., and Pastor, L. 1984. Velocity estimation from image sequences with second order differential operators,Proc. 7th Intern. Conf. Patt. Recog., Montreal, pp. 20–22.

  • Uras, S., Girosi, F., Verri, A., and Torre, V. 1988. A computational approach to motion perception,Biol. Cybern. 60: 79–97.

    Google Scholar 

  • Verri, A., and Poggio, T. 1987. Against quantitative optical flow,Proc. 1st Intern. Conf. Comput. Vis., London, pp. 171–180.

  • Watson, A.B., and Ahumada, A.J. 1985. Model of human visual-motion sensing,J. Opt. Soc. Amer. A 2: 322–342.

    Google Scholar 

  • Waxman, A.M., and Wohn, K. 1985. Contour evolution, neighbourhood deformation and global image flow: Planar surfaces in motion, Intern.J. Robotics Res. 4: 95–108.

    Google Scholar 

  • Waxman, A.M., Wu, J., and Bergholm, F. 1988. Convected activation profiles and receptive fields for real time measurement of short range visual motion,Proc. Conf. Comput. Vis. Patt. Recog., Ann Arbor, pp. 771–723.

  • Willick, D., and Yang, Y.H. 1991. Experimental evaluation of motion constraints equations.Comput. Vis. Graph. Image Process.: Image Understanding 54: 206–214.

    Google Scholar 

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Barron, J.L., Fleet, D.J. & Beauchemin, S.S. Performance of optical flow techniques. Int J Comput Vision 12, 43–77 (1994). https://doi.org/10.1007/BF01420984

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