Video Detection Algorithm Using an Optical Flow Calculation Method

  • Andrzej Głowacz
  • Zbigniew Mikrut
  • Piotr Pawlik
Part of the Communications in Computer and Information Science book series (CCIS, volume 287)


The article presents the concept and implementation of an algorithm for detecting and counting vehicles based on optical flow analysis. The effectiveness and calculation time of three optical flow algorithms (Lucas-Kanade, Horn-Schunck and Brox) were compared. Taking into account the effectiveness and calculation time the Horn-Schunck algorithm was selected and applied to separating moving objects. The authors found that the algorithm is effective at detecting objects when they are subject to binarisation using a fixed threshold. Thanks to the specialized software the results obtained by the algorithm were compared with the manual ones: the total vehicle detection and counting rate achieved by the algorithm was 95,4%. The algorithm is capable to analyse about 8 frames per second (Intel Core i7 920, 2.66 GHz processor, Win7x64).


vehicle detection vehicle counting optical flow video detector traffic analysis 


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  1. 1.
    Adamski, A., Bubliński, Z., Mikrut, Z., Pawlik, P.: The image-based automatic monitoring for safety traffic lanes intersections. In: Piecha, J. (ed.) Transactions on Transport Systems Telematics, Wyd. Politechniki Śląskiej, Gliwice, pp. 92–102 (2004)Google Scholar
  2. 2.
    Adamski, A., Mikrut, Z.: The Cracovian prototype of videodetectors feedback in transportation systems. In: Piecha, J. (ed.) Trans. on Transport Systems Telematics, Wyd. Politechniki Śląskiej, Gliwice, pp. 140–151 (2004)Google Scholar
  3. 3.
    Beauchemin, S.S., Barron, J.L.: The Computation of Optical Flow. ACM Computing Surveys 27(3), 433–467 (1995)CrossRefGoogle Scholar
  4. 4.
    Barron, J.L., Beauchemin, S.S., Fleet, D.J.: On Optical Flow. In: 6th Int. Conf. on Artificial Intelligence and Information-Control Systems of Robots, Bratislava, Slovakia, September 12-16, pp. 3–14 (1994)Google Scholar
  5. 5.
    Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. Int. Journal of Computer Vision 12(1), 43–77 (1994)CrossRefGoogle Scholar
  6. 6.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High Accuracy Optical Flow Estimation Based on a Theory for Warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Galvin, B., McCane, B., Novins, K., Mason, D., Mills, S.: Recovering Motion Fields: An Evaluation of Eight Optical Flow Algorithms. In: Proc. of the British Machine Vision Conference, BMVC (1998)Google Scholar
  8. 8.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17, 185–204 (1981)CrossRefGoogle Scholar
  9. 9.
    Horn, B.K.P., Schunck, B.G.: Determining optical flow: a retrospective. Artificial Intelligence 59, 81–87 (1993)CrossRefGoogle Scholar
  10. 10.
    Kotula, K., Mikrut, Z.: Detection and segmentation of vehicles based on a hierarchical “optical flow” algorithm. Trans. on Transport Systems Telematics, 34–46 (2006)Google Scholar
  11. 11.
    Liu, H., Hong, T., Herman, M., Camus, T., Chellappa, R.: Accuracy vs. Efficiency Trade-offs in Optical Flow Algorithms. Computer Vision and Image Understanding (CVIU) 72(3), 271–286 (1998)CrossRefGoogle Scholar
  12. 12.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. 7th Intl. Joint Conf. on Artificial Intelligence (IJACAI), Vancouver, August 24-28, pp. 674–679 (1981)Google Scholar
  13. 13.
    Mikrut, Z.: Road Traffic Measurement Using Videodetection. Image Processing and Communications 3(3-4), 19–30 (1997)Google Scholar
  14. 14.
    Mikrut, Z.: The Cracovian Videodetector - from Ideas to Embedding. In: Proc. Int. Conf. Transportation and Logistics Integrated Systems ITS-ILS 2007, Kraków, pp. 29–37 (2007)Google Scholar
  15. 15.
    Mikrut, Z., Pałczyński, K.: Image sequences segmentation based on optical flow method. Automatyka AGH 7(3), 371–384 (2003) (in Polish)Google Scholar
  16. 16.
    Sand, P., Teller, S.: Particle video. In: IEEE Computer Vision and Pattern Recognition, CVPR (2006)Google Scholar
  17. 17.
    Tadeusiewicz, R.: How Intelligent Should Be System for Image Analysis? In: Kwasnicka, H., Jain, L.C. (eds.) Innovations in Intelligent Image Analysis. SCI, vol. 339, pp. V – X. Springer, Heidelberg (2011)Google Scholar
  18. 18.
    Chari, V.: High Accuracy Optical Flow Using a Theory for Warping, (accessed March 20, 2012)
  19. 19.
    INSIGMA Project. AGH UST, Kraków, (accessed February 4, 2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrzej Głowacz
    • 1
  • Zbigniew Mikrut
    • 2
  • Piotr Pawlik
    • 2
  1. 1.Department of TelecommunicationsAGH University of Science and TechnologyKrakówPoland
  2. 2.Department of AutomaticsAGH University of Science and TechnologyKrakówPoland

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