Abstract
Optical flow estimation by means of first derivatives can produce surprisingly accurate and dense optical flow fields. In particular, recent empirical evidence suggests that the method that is based on local optimization of first-order constancy constraints is among the most accurate and reliable methods available. Nevertheless, a systematic investigation of the effects of the various parameters for this algorithm is still lacking. This paper reports such an investigation. Performance is assessed in terms of flow-field accuracy, density, and resolution. The investigation yields new information regarding pre-filter, differentiator, least-squares neighborhood, and reliability test selection. Several changes to previously-employed parameter settings result in significant overall performance improvements, while they simultaneously reduce the computational cost of the estimator.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adelson, E. H. and Bergen, J. R. 1986. The extraction of spatiotemporal energy in human and machine vision. In Proc. IEEE Workshop on Visual Motion, pp. 151-156.
Barron, J. L., Fleet, D. J., Beauchemin, S. S., and Burkitt, T. A. 1993. Performance of optical flow techniques. Technical Report TR-299, Dept. of Computer Science, Univ. of Western Ontario, July 1992. Revised July 1993.
Barron, J. L., Fleet, D. J., and Beauchemin, S. S. 1994. Performance of optical flow techniques. Int. J. Computer Vision, 12(1):43-77.
Brandt, J. W. 1994a. Analysis of bias in gradient-based optical-flow estimation. In Proc. Twenty-Eighth Annual Asilomar Conference on Signals, Systems, and Computers, pp. 721-725.
Brandt, J. W. 1994b. Derivative-based optical flow estimation: Controlled comparison of first-and second-order methods. In IAPR Workshop on Machine Vision Applications, pp. 464-469.
Brandt, J. W. 1994c. Finite-differencing errors in gradient-based optical flow estimation. In Proc. First IEEE Int. Conf. Image Processing, vol. II, pp. 775-779.
Cafforio, C. 1982. Remarks on the differential method for estimation of movement in television images. Signal Processing, 4:45-52.
Cafforio, C. and Rocca, F. 1979. Tracking moving objects in television images. Signal Processing, 1:133-140.
Campani, M. and Verri, A. 1990. Computing optical flow from an overconstrained system of linear algebraic equations. In Proc. Third IEEE Int. Conf. Computer Vision, pp. 22-26.
Enkelmann, W. 1988. Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences. Computer Vision, Graphics, and Image Processing, 43:150-177.
Fleet, D. J. and Jepson, A. D. 1990. Computation of component image velocity from local phase information. Int. J. Computer Vision, 5:77-104.
Heeger, D. J. 1987. Model for the extraction of image flow. J. Optical Soc. America A, 4(8):1455-1471.
Horn, B. K. P. and Shunck, B. G. 1981. Determining optical flow. Artificial Intelligence, 17:185-203.
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. Pattern Analysis and Machine Intelligence, 9(2):229-244.
Lucas, B. and Kanade, T. 1981a. An iterative image registration technique with an application to stereo vision. In Proc. DARPA Image Understanding Workshop, pp. 121-130.
Lucas, B. and Kanade, T. 1981b. An iterative image registration technique with an application to stereo vision. In Proc. 5th Int. Joint Conf. Artificial Intelligence, pp. 674-679.
Lucas, B. and Kanade, T. 1985. Optical navigation by the method of differences. In Proc. 7th Int. Joint Conf. Artificial Intelligence, pp. 981-984.
Nagel, H.-H. 1987. On the estimation of optical flow: Relations between different approaches and some new results. Artificial Intelligence, 33:299-324.
Nomura, A., Miike, H., and Koga, K. 1993. Detecting a velocity field from sequential images under time-varying illumination. In Time-Varying Image Processing and Moving Object Recognition, V. Cappellini (Ed.), Elsevier, vol. 3, pp. 343-350.
Otte, M. and Nagel, H.-H. 1994. Optical flow estimation: Advances and comparisons. In Lecture Notes in Computer Science, ECCV '94, Jan-Olof Eklundh (Ed.), vol. 800, pp. 51-60.
Simoncelli, E. P. 1993. Distributed Representation and Analysis of Visual Motion. Ph. D. Thesis, Dept. of Electrical Engineering and Computer Science, MIT.
Simoncelli, E. P. 1994. Design of multi-dimensional derivative filters. In Proc. First IEEE Int. Conf. Image Processing, vol. I, pp. 790- 779.
Simoncelli, E. P., Adelson, E. H., and Heeger, D. J. 1991. Probability distributions of optical flow. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 310-315.
Tistarelli, M. 1994. Multiple constraints for optical flow. In Lecture Notes in Computer Science, ECCV '94, Jan-Olof Eklundh (Ed.), vol. 800, pp. 61-70.
Uras, S., Girosi, F., Verri, A., and Torre, V. 1988. A computational approach to motion perception. Biol. Cybern., 60:79- 87.
Verri, A., Girosi, F., and Torre, V. 1990. Differential techniques for optical flow. J. Opt. Soc. Am. A, 7(5):912-922.
Weber, J. and Malik, J. 1993. Robust computation of optical flow in a multi-scale differential framework. Proc. IEEE Int. Conf. Computer Vision, pp. 12-20.
Weber, J. and Malik, J. 1995. Robust computation of optical flow in a multi-scale differential framework. Int. J. Computer Vision, 14:67-81.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Brandt, J.W. Improved Accuracy in Gradient-Based Optical Flow Estimation. International Journal of Computer Vision 25, 5–22 (1997). https://doi.org/10.1023/A:1007987001439
Issue Date:
DOI: https://doi.org/10.1023/A:1007987001439