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Robust Estimation of Camera Motion Using Optical Flow Models

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Advances in Visual Computing (ISVC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5875))

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Abstract

The estimation of camera motion is one of the most important aspects for video processing, analysis, indexing, and retrieval. Most of existing techniques to estimate camera motion are based on optical flow methods in the uncompressed domain. However, to decode and to analyze a video sequence is extremely time-consuming. Since video data are usually available in MPEG-compressed form, it is desirable to directly process video material without decoding. In this paper, we present a novel approach for estimating camera motion in MPEG video sequences. Our technique relies on linear combinations of optical flow models. The proposed method first creates prototypes of optical flow, and then performs a linear decomposition on the MPEG motion vectors, which is used to estimate the camera parameters. Experiments on synthesized and real-world video clips show that our technique is more effective than the state-of-the-art approaches for estimating camera motion in MPEG video sequences.

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References

  1. Chang, S.F., Chen, W., Meng, H.J., Sundaram, H., Zhong, D.: A fully automated content-based video search engine supporting spatio-temporal queries. IEEE Trans. Circuits Syst. Video Techn. 8, 602–615 (1998)

    Article  Google Scholar 

  2. Hampapur, A., Gupta, A., Horowitz, B., Shu, C.F., Fuller, C., Bach, J.R., Gorkani, M., Jain, R.: Virage video engine. In: Storage and Retrieval for Image and Video Databases (SPIE), pp. 188–198 (1997)

    Google Scholar 

  3. Ponceleon, D.B., Srinivasan, S., Amir, A., Petkovic, D., Diklic, D.: Key to effective video retrieval: Effective cataloging and browsing. In: ACM Multimedia, pp. 99–107 (1998)

    Google Scholar 

  4. Kim, J.G., Chang, H.S., Kim, J., Kim, H.M.: Efficient camera motion characterization for mpeg video indexing. In: ICME, pp. 1171–1174 (2000)

    Google Scholar 

  5. Dufaux, F., Konrad, J.: Efficient, robust, and fast global motion estimation for video coding. IEEE Trans. Image Process. 9, 497–501 (2000)

    Article  Google Scholar 

  6. Park, S.C., Lee, H.S., Lee, S.W.: Qualitative estimation of camera motion parameters from the linear composition of optical flow. Pattern Recognition 37, 767–779 (2004)

    Article  Google Scholar 

  7. Qi, B., Ghazal, M., Amer, A.: Robust global motion estimation oriented to video object segmentation. IEEE Trans. Image Process. 17, 958–967 (2008)

    Article  MathSciNet  Google Scholar 

  8. Sand, P., Teller, S.J.: Particle video: Long-range motion estimation using point trajectories. IJCV 80, 72–91 (2008)

    Article  Google Scholar 

  9. Srinivasan, M.V., Venkatesh, S., Hosie, R.: Qualitative estimation of camera motion parameters from video sequences. Pattern Recognition 30, 593–606 (1997)

    Article  Google Scholar 

  10. Zhang, T., Tomasi, C.: Fast, robust, and consistent camera motion estimation. In: CVPR, pp. 1164–1170 (1999)

    Google Scholar 

  11. Minetto, R., Leite, N.J., Stolfi, J.: Reliable detection of camera motion based on weighted optical flow fitting. In: VISAPP, pp. 435–440 (2007)

    Google Scholar 

  12. Gillespie, W.J., Nguyen, D.T.: Robust estimation of camera motion in MPEG domain. In: TENCON, pp. 395–398 (2004)

    Google Scholar 

  13. Tiburzi, F., Bescos, J.: Camera motion analysis in on-line MPEG sequences. In: WIAMIS, pp. 42–45 (2007)

    Google Scholar 

  14. Smolic, A., Hoeynck, M., Ohm, J.R.: Low-complexity global motion estimation from p-frame motion vectors for mpeg-7 applications. In: ICIP, pp. 271–274 (2000)

    Google Scholar 

  15. Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. John Wiley and Sons, Inc., Chichester (1987)

    Book  MATH  Google Scholar 

  16. Tan, Y.-P., Saur, D.D., Kulkarni, S.R., Ramadge, P.J.: Rapid estimation of camera motion from compressed video with application to video annotation. IEEE Trans. Circuits Syst. Video Techn. 10, 133–146 (2000)

    Article  Google Scholar 

  17. Ewerth, R., Schwalb, M., Tessmann, P., Freisleben, B.: Estimation of arbitrary camera motion in MPEG videos. In: ICPR, pp. 512–515 (2004)

    Google Scholar 

  18. Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  19. Ma, Y., Soatto, S., Kosecka, J., Sastry, S.S.: An Invitation to 3-D Vision: From Images to Geometric Models. Springer, Heidelberg (2003)

    Google Scholar 

  20. Martin, J., Crowley, J.L.: Experimental comparison of correlation techniques. In: Int. Conf. on Intelligent Autonomous Systems (1995)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computing, University of Campinas, Brazil

    Jurandy Almeida, Rodrigo Minetto, Ricardo da S. Torres & Neucimar J. Leite

  2. School of Electrical and Computer Engineering, University of Campinas, Brazil

    Tiago A. Almeida

Authors
  1. Jurandy Almeida
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  2. Rodrigo Minetto
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  3. Tiago A. Almeida
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  4. Ricardo da S. Torres
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  5. Neucimar J. Leite
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Graduate School of Science and Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama-shi, 338-8570, Saitama, Japan

    Yoshinori Kuno

  6. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, P.O. Box, 119613, Singapore

    Junxian Wang

  7. Department of Computer Science & Information Engineering, Tamkang University, Tamsui, Taipei, Taiwan, R.O.C.

    Jun-Xuan Wang

  8. Microsoft Research, Redmond, WA, USA

    Junxian Wang

  9. Department of Informatics, Univ. of Zurich, Winterthurerstr. 190, P.O. Box, 8057, Zurich, Switzerland

    Renato Pajarola

  10. Lawrence Livermore National Laboratory, 94550, Livermore, CA, USA

    Peter Lindstrom

  11. University of Applied Sciences Bonn-Rhein-Sieg, 53754, Sankt Augustin, Germany

    André Hinkenjann

  12. Humana Inc., 40202, Louisville, KY, USA

    Miguel L. Encarnação

  13. SCI Institute & School of Computing, University of Utah, 84112, Salt Lake City, UT, USA

    Cláudio T. Silva

  14. Desert Research Institute, 89512, Reno, NV, USA

    Daniel Coming

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© 2009 Springer-Verlag Berlin Heidelberg

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Almeida, J., Minetto, R., Almeida, T.A., da S. Torres, R., Leite, N.J. (2009). Robust Estimation of Camera Motion Using Optical Flow Models. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_41

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  • DOI: https://doi.org/10.1007/978-3-642-10331-5_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10330-8

  • Online ISBN: 978-3-642-10331-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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