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Prior-Free Dependent Motion Segmentation Using Helmholtz-Hodge Decomposition Based Object-Motion Oriented Map

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Abstract

Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However, existing work depends on prior-knowledge on the camera motion and scene structure for model selection. This is not always available in practice. Moreover, the image plane motion suffers from depth variations, which leads to depth-dependent motion segmentation in 3D scenes. To solve these problems, this paper develops a prior-free dependent motion segmentation algorithm by introducing a modified Helmholtz-Hodge decomposition (HHD) based object-motion oriented map (OOM). By decomposing the image motion (optical flow) into a curl-free and a divergence-free component, all kinds of camera-induced image motions can be represented by these two components in an invariant way. HHD identifies the camera-induced image motion as one segment irrespective of depth variations with the help of OOM. To segment object motions from the scene, we deploy a novel spatio-temporal constrained quadtree labeling. Extensive experimental results on benchmarks demonstrate that our method improves the performance of the state-of-the-art by 10%~20% even over challenging scenes with complex background.

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References

  1. Cucchiara R, Prati A, Vezzani R. Real-time motion segmentation from moving cameras. Real-Time Imaging, 2004, 10(3): 127-143.

    Article  Google Scholar 

  2. Li H, Wu W, Wu E. Robust interactive image segmentation via graph-based manifold ranking. Computational Visual Media, 2015, 1(3): 183-195.

    Article  Google Scholar 

  3. Zhang F L, Wang J, Zhao H, Martin R R, Hu SM. Simultaneous camera path optimization and distraction removal for improving amateur video. IEEE Transactions on Image Processing, 2015, 24(12): 5982-5994.

    Article  MathSciNet  Google Scholar 

  4. Zhang F L, Wu X, Zhang H T, Wang J, Hu S M. Robust background identification for dynamic video editing. ACM Transactions on Graphics, 2016, 35(6): 197:1-197:12.

  5. Zhang Y, Tang Y L, Cheng K L. Efficient video cutout by paint selection. Journal of Computer Science and Technology, 2015, 30(3): 467-477.

    Article  Google Scholar 

  6. Zografos V, Nordberg K. Fast and accurate motion segmentation using linear combination of views. In Proc. the 22nd British Machine Vision Conference (BMVC), Aug.29-Sept.2, 2011.

  7. Aldroubi A. A review of subspace segmentation: Problem, nonlinear approximations, and applications to motion segmentation. ISRN Signal Process, 2013, Article ID 4174492.

  8. Ichimura N. Motion segmentation based on factorization method and discriminant criterion. In Proc. the 7th IEEE International Conference on Computer Vision (ICCV), September 1999, pp.600-605.

  9. Vidal R, Ma Y, Soatto S et al. Two-view multibody structure from motion. International Journal of Computer Vision (IJCV), 2006, 68(1): 7-25.

    Article  Google Scholar 

  10. Sugaya Y, Kanatani K. Geometric structure of degeneracy for multi-body motion segmentation. In Proc. the 2nd International Workshop on Statistical Methods in Video Processing, May 2004, pp.13-25.

  11. Costeira J, Kanade T. A multibody factorization method for independently moving objects. International Journal of Computer Vision (IJCV), 1998, 29(3): 159-179.

    Article  Google Scholar 

  12. Kanatani K, Matsunaga C. Estimating the number of independent motions for multibody motion segmentation. In Proc. the 5th Asian Conference on Computer Vision, February 2002, pp.7-12.

  13. Shi F, Zhou Z, Xiao J, Wu W. Robust trajectory clustering for motion segmentation. In Proc. the IEEE International Conference on Computer Vision (ICCV), December 2013, pp.3088-3095.

  14. Ochs P, Malik J, Brox T. Segmentation of moving objects by long term video analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014, 36(6): 1187-1200.

    Article  Google Scholar 

  15. Su Y, Sun M T, Hsu V. Global motion estimation from coarsely sampled motion vector field and the applications. IEEE Transactions on Circuits System and Video Technology, 2005, 15(2): 232-242.

    Article  Google Scholar 

  16. Smolic A, Hoeynck M, Ohm J R. Low-complexity global motion estimation from P-frame motion vectors for MPEG-7 applications. In Proc. the International Conference on Image Processing (ICIP), September 2000, pp.271-274.

  17. Chen Y M, Bajic I V. Motion vector outlier rejection cascade for global motion estimation. IEEE Signal Processing Letters, 2010, 17(2): 197-200.

    Article  Google Scholar 

  18. Fischler M, Bolles R. RANSAC random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981, 24(6): 381-395.

    Article  MathSciNet  Google Scholar 

  19. Chen Y M, Bajic I V. A joint approach to global motion estimation and motion segmentation from a coarsely sampled motion vector field. IEEE Transactions on Circuits Systtem and Video Technology, 2011, 21(9): 1316-1328.

    Article  Google Scholar 

  20. Qian C, Bajić I V. Global motion estimation under translation-zoom ambiguity. In Proc. the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), August 2013, pp.46-51.

  21. Narayana M, Hanson A, Learned-Miller E. Coherent motion segmentation in moving camera videos using optical flow orientations. In Proc. the IEEE International Conference on Computer Vision (ICCV), December 2013, pp.1577-1584.

  22. Tong Y, Lombeyda S, Hirani A N, Desbrun M. Discrete multiscale vector field decomposition. ACM Transactions on Graphics (TOG), 2003, 22(3): 445-452.

    Article  Google Scholar 

  23. Polthier K, Preuβ E. Identifying vector fields singularities using a discrete Hodge decomposition. In Visualization and Mathematics III., Hege H C, Polthier K (eds.), Springer, 2003, pp.123-134.

  24. Irani M, Anandan P. A unified approach to moving object detection in 2D and 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(6): 577-589.

    Article  Google Scholar 

  25. Brox T, Bruhn A, Papenberg N, Weickert J. High accuracy optical flow estimation based on a theory for warping. In Proc. the 8th European Conference on Computer Vision (ECCV), May 2004, pp.25-36.

  26. von Helmholtz H. über integrale der hydrodynamischen Gleichungen, welche den wirbelbewegungen entsprechen. Journal Für Die Reine Und Angewandte Mathematik, 2010, 55(1858): 25-55. (in German)

  27. Bhatia H, Norgard G, Pascucci V, Bremer P T. The Helmholtz-Hodge Decomposition — A survey. IEEE Transactions on Visualization and Computer Graphics (TVCG), 2013, 19(8): 1386-1404.

    Article  Google Scholar 

  28. Baker S, Scharstein D, Lewis J P, Roth S, Black M J, Szeliskiv R. A database and evaluation methodology for optical flow. International Journal of Computer Vision, 2011, 92(1): 1-31.

    Article  Google Scholar 

  29. Liang X, Zhang C, Matsuyama T. Inlier estimation for moving camera motion segmentation. In Proc. the 12th Asian Conference on Computer Vision (ACCV), November 2014, pp.352-367.

  30. Liang X, Zhang C, Matsuyama T. A general inlier estimation for moving camera motion segmentation. IPSJ Transactions on Computer Vision and Applications, 2015, 7: 163-174.

    Article  Google Scholar 

  31. Tron R, Vidal R. A benchmark for the comparison of 3-D motion segmentation algorithms. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2007.

  32. Brox T, Malik J. Object segmentation by long term analysis of point trajectories. In Proc. the 11th European Conference on Computer Vision (ECCV), September 2010, pp.282-295.

  33. Tsai D, Flagg M, Rehg J. Motion coherent tracking with multi-label MRF optimization. In Proc. the British Machine Vision Conference, Aug.31-Sept.3, 2010.

  34. Liang X, McOwan P, Johnston A. A biologically inspired framework for spatial and spectral velocity estimations. Journal of the Optical Society of America A, 2011, 28(4): 713-723.

    Article  Google Scholar 

  35. Yan J, Pollefeys M. A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and non-degenerate. In Proc. the 9th European Conference on Computer Vision (ECCV), May 2006, pp.94-106.

  36. Dembczynski K, Jachnik A, Kotlowski W, Waegeman W, Hullermeier E. Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization. In Proc. the 30th International Conference on Machine Learning (ICML), June 2013, pp.1130-1138.

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Correspondence to Zhi-Lei Liu.

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Zhang, CC., Liu, ZL. Prior-Free Dependent Motion Segmentation Using Helmholtz-Hodge Decomposition Based Object-Motion Oriented Map. J. Comput. Sci. Technol. 32, 520–535 (2017). https://doi.org/10.1007/s11390-017-1741-z

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