1 / 23

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.

parry
Télécharger la présentation

Motion Segmentation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related