Motion Segmentation
description
Transcript of Motion Segmentation
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Motion Segmentation
CAGD&CG SeminarWanqiang Shen
2008-04-09
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Application
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Motion analysis
Initialization
Motion detection
Motion tracing Pose estimation Recognition
Motion segmentati
on
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Problem
Motion segmentation
projections
clusters
How much
What
How
Accurate Robust Fast
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Traditional model A rigid-body motion
Multiple rigid-body motions
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Paper [1] R. Vidal, Y. Ma, and S. Sastry. Generalized Principal Comp
onent 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.
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[1] GPCA
Model
Estimating n Estimating subspaces Optimizing & clustering
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[1] Model
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[1] Estimating n
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[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
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[1] Optimizing & clustering
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[1] example
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[1] Remarks Advantages
Algebraic algorithm Dealing with both independent and dependent motions
disadvantages Deteriorating as n increases C is sensitive to outliers
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[2] LSA
clustering
projection
local subspace estimation
SVD
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[2] Projection2 2
TF P F K K K K PW U D V
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[2] Local subspace estimation
Affinity matrix
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[2] Clustering Estimation N While Numofclusters< N
Compute affinity matrix for each clusters Divide each cluster into two clusters Evaluate the best subdivision
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[2] examples
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[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
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[3] test samples
checkerboard traffic articulated
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[3] Benchmark
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[3] comparing data
accuracy GPCA LSA
Check. 6.09% 5.71%
Traffic 1. 41% 3.75%
Articul. 2.88% 4.38%
All 4.59% 5.09%
time GPCA LSA
Check. 353ms 7.762s
Traffic 288ms 6.787s
Articul. 224ms 4.002s
All 324ms 7.165s
Two groups
Three groupsaccuracy GPCA LSA
Check. 31.95% 18.09%
Traffic 19.83% 26.05%
Articul. 16.85% 15.18%
All 28.66% 19.51%
time GPCA LSA
Check. 842ms 17.314s
Traffic 529ms 12.746s
Articul. 125ms 1.288s
All 738ms 15.485s
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Thank you!