Skip to main content

Automatic Moving Object Segmentation with Accurate Boundaries

  • Conference paper
Computer Vision – ACCV 2006 (ACCV 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3851))

Included in the following conference series:

  • 1622 Accesses

Abstract

This paper presents a layer-model based method to segment moving objects from image sequence with accurate boundaries. The segmentation framework involves three stages: Motion seed detection, Motion layer expansion and Motion boundary refinement. In the first stage, motion seeds, which determine the amount and initial position of motion layers, are detected by corner matching between consecutive frames, and classified by global motion analysis. In the second stage, the detected motion seeds are expanded into motion layers. To preserve the spatial continuity, an energy function is defined to evaluate the spatial smoothness and accuracy of the layers. Then, Graph Cuts technique is used to solve the energy minimization problem and extract motion layers. In the last stage, the extracted layers are combined with edge information to find accurate boundaries of moving objects. The proposed method is tested on several image sequences and the experimental results illustrate its promising performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Arch, T., Kaup, A.: Statistical model-based change detection in moving video. Signal Processing 31, 165–180 (1993)

    Article  Google Scholar 

  2. Mech, R., Wollborn, M.: A noise robust method for 2d shape estimation of moving objects in video sequences considering a moving camera. Signal Processing 66, 203–217 (1998)

    Article  MATH  Google Scholar 

  3. Meier, T., Ngan, K.: Automatic segmentation of movings for video object plane generation. IEEE Trans. on Circuits & Systems for Video Technology 8, 525–538 (2003)

    Article  Google Scholar 

  4. Nicolescu, M., Medioni, G.: Motion segmentation with accurate boundaries-a tensor voting approach. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 382–389 (2003)

    Google Scholar 

  5. Wang, J., Adelson, E.: Layered representation for motion analysis. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1993)

    Google Scholar 

  6. Darrell, T., Pentland, A.: Cooperative robust estimation using layers of support. IEEE Trans. on Pattern Analysis and Machine Intelligence 17, 474–487 (1995)

    Article  Google Scholar 

  7. Ayer, S., Sawhney, H.: Layered representation of motion video using robust maximum-likelihood estimation of mixture models and mdl encoding. In: 5th International Conference on Computer Vision (1995)

    Google Scholar 

  8. Jepson, A., Fleet, D., Black, M.: A layered motion representation with occlusion and compact spatial support. In: 7th European Conference on Computer Vision, vol. 1, pp. 692–706 (2002)

    Google Scholar 

  9. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)

    Article  Google Scholar 

  10. Wang, J., Lu, H., Liu, Q.: Moving object segmentation using graph cuts. In: IEEE Int’l Conf. on Signal Processing, vol. 1, pp. 777–780 (2004)

    Google Scholar 

  11. Vincent, L., Soille, P.: Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. on Pattern Analysis and Machine Intelligence 13, 583–598 (1991)

    Article  Google Scholar 

  12. Wang, J., Lu, H., Liu, Q.: A fast region merging algorithm for watershed segmentation. In: IEEE Int’l Conf. on Signal Processing, vol. 1, pp. 781–784 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, J., Wang, H., Liu, Q., Lu, H. (2006). Automatic Moving Object Segmentation with Accurate Boundaries. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_29

Download citation

  • DOI: https://doi.org/10.1007/11612032_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31219-2

  • Online ISBN: 978-3-540-32433-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics