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Motion Segmentation

Motion Segmentation. CAGD&CG Seminar Wanqiang Shen 2008-04-09. Application. Initialization. Motion detection. Motion tracing. Pose estimation. Recognition. Motion analysis. Motion segmentation. Accurate Robust Fast. projections. Motion segmentation. clusters. Problem. How much.

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Motion Segmentation

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  1. Motion Segmentation CAGD&CG Seminar Wanqiang Shen 2008-04-09

  2. Application

  3. Initialization Motion detection Motion tracing Pose estimation Recognition Motion analysis Motion segmentation

  4. Accurate Robust Fast projections Motion segmentation clusters Problem How much What How

  5. A rigid-body motion Traditional model • Multiple rigid-body motions

  6. Paper [1] R. Vidal, Y. Ma, and S. Sastry. Generalized Principal Component Analysis (GPCA). IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(12):1–15, 2005. [2] J. Yan and M. Pollefeys. A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and non-degenerate. In European Conference on Computer Vision, pages 94–106, 2006. [3] R. Tron and R. Vidal: A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms. IEEE International Conference on Computer Vision and Pattern Recognition, 2007.

  7. [1] GPCA Estimating n Estimating subspaces Optimizing & clustering Model

  8. [1] Model

  9. [1] Estimating n

  10. [1] Estimating subspaces • calculating normalized C • Factorization • Solving for the last 2 entries of each bi • Solving for the first K-2 entries of each bi

  11. [1] Optimizing & clustering

  12. [1] example

  13. [1] Remarks • Advantages • Algebraic algorithm • Dealing with both independent and dependent motions • disadvantages • Deteriorating as n increases • C is sensitive to outliers

  14. [2] LSA clustering projection SVD local subspace estimation

  15. [2] Projection

  16. [2] Local subspace estimation Affinity matrix

  17. [2] Clustering • Estimation N • While Numofclusters< N • Compute affinity matrix for each clusters • Divide each cluster into two clusters • Evaluate the best subdivision

  18. [2] examples

  19. [2] Remarks • Advantages • Outliers are likely to be “rejected” • Need less point trajectories • disadvantages • Neighbors of a point belong to different subspace • The select neighbors may not span the underlying subspace

  20. [3] test samples checkerboard traffic articulated

  21. [3] Benchmark

  22. [3] comparing data • Two groups • Three groups

  23. Thank you!

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