II-LK – A Real-Time Implementation for Sparse Optical Flow

  • Tobias Senst
  • Volker Eiselein
  • Thomas Sikora
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6111)

Abstract

In this paper we present an approach to speed up the computation of sparse optical flow fields by means of integral images and provide implementation details. Proposing a modification of the Lucas-Kanade energy functional allows us to use integral images and thus to speed up the method notably while affecting only slightly the quality of the computed optical flow. The approach is combined with an efficient scanline algorithm to reduce the computation of integral images to those areas where there are features to be tracked. The proposed method can speed up current surveillance algorithms used for scene description and crowd analysis.

Keywords

Lucas-Kanade optical flow fast implementation integral images optimization real-time 

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References

  1. 1.
    Baker, S., Matthews, I.: Lucas-Kanade 20 Years on: A Unifying Framework. International Journal of Computer Vision 56, 221–255 (2004)CrossRefGoogle Scholar
  2. 2.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A Database and Evaluation Methodology for Optical Flow. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)Google Scholar
  3. 3.
    Birchfield, S., Pundlik, S.J.: Joint tracking of features and edges. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–6. IEEE Computer Society, Alaska (2008)Google Scholar
  4. 4.
    Bouguet, J.Y.: Pyramidal Implementation of the Lucas Kanade Feature Tracker - Description of the Algorithm. Technical report, Intel Corporation, Microprocessor Research Labs (1999)Google Scholar
  5. 5.
    Brostow, G.J., Cipolla, R.: Unsupervised Bayesian Detection of Independent Motion in Crowds. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 594–601 (2006)Google Scholar
  6. 6.
    Bruhn, A., Weickert, J., Feddern, C., Kohlberger, T., Schnörr, C.: Variational optical flow computation in real time. IEEE Transactions on Image Processing 14, 608–615 (2005)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Bruhn, A., Weickert, J., Schnörr, C.: Combining the Advantages of Local and Global Optic Flow Methods. In: Proceedings of the 24th DAGM Symposium on Pattern Recognition, pp. 454–462. Springer, London (2002)Google Scholar
  8. 8.
    Crow, F.C.: Summed-area tables for texture mapping. In: SIGGRAPH 1984: Proceedings of the 11th annual conference on Computer graphics and interactive techniques, pp. 207–212. ACM, New York (1984)CrossRefGoogle Scholar
  9. 9.
    Ercan, A.O., Guibas, L.J.: Object tracking in the presence of occlusions via a camera network. In: IPSN 2007: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, pp. 509–518. ACM Press, New York (2007)CrossRefGoogle Scholar
  10. 10.
    Hong, H.S., Chung, M.J.: 3D pose and camera parameter tracking algorithm based on Lucas-Kanade image alignment algorithm. In: International Conference on Control, Automation and Systems, ICCAS, pp. 548–551 (2007)Google Scholar
  11. 11.
    Landgraf, T., Rojas, R.: Tracking honey bee dances from sparse optical flow fields. Technical report (2007)Google Scholar
  12. 12.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: 7th International Joint Conference on Artificial Intelligence, pp. 674–679. William Kaufmann, Vancouver (1981)Google Scholar
  13. 13.
    Paul, V., Michael, J.: Robust Real-time Object Detection. International Journal of Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  14. 14.
    Saxena, S., Brémont, F., Thonnat, M., Ma, R.: Crowd Behavior Recogniton for Video Surveillance. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 970–981. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Shi, J., Tomasi, C.: Good Features to Track. Technical report, Cornell University (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tobias Senst
    • 1
  • Volker Eiselein
    • 1
  • Thomas Sikora
    • 1
  1. 1.Communication Systems GroupTechnische Universität Berlin 

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