Abstract
Optical flow algorithms generally demand for high computational power and huge storage capacities. This paper is a contribution for real-time implementation of an optical flow algorithm on a pipeline machine. This overall optical flow computation methodology is presented and evaluated on a set of synthetic and real image sequences. Results are compared to other implementations using as measures the average angular error, the optical flow density and the root mean square error. The proposed implementation achieves very low computation delays, allowing operation at standard video frame-rate and resolution. It compares favorably to recent implementations in standard microprocessors and in parallel hardware.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Smith, A.T., Snowden, R.J.: Visual Detection of Motion. Academic Press, New York (1994)
Cummings, R.: Biologically inspired visual motion detection in VLSI. International Journal of Computer Vision 44, 175–198 (2001)
Duric, Z., Rosenfeld, A., Duncan, J.: The applicability of Green’s theorem to computation of rate of approach. International Journal of Computer Vision 31(1), 83–98 (1999)
Stofler, N., Burkert, T., Farber, G.: Real-time obstacle avoidance using MPE Gprocessor- based optic flow sensor. In: Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, pp. 161–166 (2000)
Liu, H., Hong, T.-H., Herman, M., Camus, T.: Accuracy vs efficiency trade-offs in optical flow algorithms. Computer Vision and Image Understanding 72(3), 271–286 (1998)
Farnebäck, G.: Fast and accurate motion estimation using orientation tensors and parametric motion models. In: Proceedings 15th Int. Conf. on Pattern Recognition, Barcelona, Spain, pp. 135–139 (2000)
Fleury, M., Clark, A.F., Downton, A.C.: Evaluating optical-flow algorithms on a parallel machine. Image and Vision Computing 19, 131–143 (2001)
Correia, M.V., Campilho, A.C., Santos, J.A., Nunes, L.B.: Optical flow techniques applied to the calibration of visual perception experiments. In: Proceedings 13th Int. Conf. on Pattern Recognition, ICPR 1996, Vienna, Austria, pp. 498–502 (1996)
Correia, M.V., Campilho, A.C.: Real-time implementation of an optical flow algorithm. In: Proceedings 16th Int. Conf. on Pattern Recognition, Québec City, Canada, pp. 247–250 (2002)
Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–77 (1994)
Simoncelli, E.P.: Distributed representation and analysis of visual motion, Ph.D. thesis, Massachusetts Institute of Technology (January 1993)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vancouver, Canada, pp. 674–679 (1981)
Fleet, D.J., Langley, K.: Recursive filters for optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(1), 61–67 (1995)
Olson, T.J., Taylor, J.R., Lockwood, R.J.: Programming a pipelined image processor. Computer Vision and Image Understanding 64(3), 351–367 (1996)
Lin, T., Barron, J.L.: Image reconstruction error for optical flow. In: Proceedings of Vision Interface 1994, Banff National Park, Canada, pp. 73–80 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Correia, M.V., Campilho, A. (2004). A Pipelined Real-Time Optical Flow Algorithm. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-30126-4_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23240-7
Online ISBN: 978-3-540-30126-4
eBook Packages: Springer Book Archive