Monocular 3-D Tracking of the Golf Swing
Transcript of Monocular 3-D Tracking of the Golf Swing
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*Computer Vision Laboratory (CVLab), EPFL, 1015 Lausanne, Switzerland °Department Computer Science, University of Toronto, M5S 3H5, Canada
ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE
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Monocular 3-D Tracking of the Golf Swing Raquel Urtasun*, David J. Fleet° and Pascal Fua*
Problem: Model-based 3D people tracking from monocular video.
Approach: Deterministic nonlinear optimization based on subspace model for pose trajectories detection of key poses for automatic initialization background subtraction 3-frame correspondence
Result: With generic optimization methods the tracker fits smooth motions to monocular video. A multiple hypothesis tracker (e.g., particle filter) is not needed to resolve ambiguities.
This work was supported (in part) by the Swiss National Science Foundation. http://cvlab.epfl.ch/~rurtasun
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Tracking as Deterministic Hill-Climbing
Minimize
With respect to
Tracking Objective function
1) Consistency with 2D joint detection
2) Silhouette consistency: Reprojection inside mask
3) 2D Correspondences in a 3 frame bundle adjustment
[ ]ffm GG ,...,,,...,,,..., 111 µµααφ =
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∂F∂α i
=∂θ j
∂α i
∂F∂θ jj=1
ndof
∑ ∂F∂µt
=∂θ j
∂µt
∂F∂θ jj=1
ndof
∑∂θ j
∂µt
= α i
∂Θij
∂µti=1
m
∑∂θ j
∂α i
=Θij
Tracking a golf swing
Conclusion
Deterministic approach Fully 3D motion Lower cost than probabilistic Applications: Sport, Orthopedics Motion synthesis
Complementarity of the function terms
Initialization
Mean motion
Tracking an approach swing
Future work
Larger database Non linear models Multi-activity database
Prior Motion Model PCA is used to learn a subspace model for pose trajectories (ie. sequences of joint angles from kinematic human model). 10 examples same subject CMU database PCA: An eigenvector is the whole motion
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ψ = ψµ1,…,ψµN[ ]T ≈ Θ0 + α ii=1
m
∑ Θi
[ ] )()()(1
0 ti
m
iit
Ttt µαµψµψ µ Θ+Θ≈= ∑
=
α: style coefficient
µ: virtual time G: global motion
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F = wtypei
i=1
nobs
∑ Obstypei (xi,S)2
+ wG Gt − ˆ G t2
+ wµ µt − ˆ µ t( )2+ wµ µt − ˆ µ t( )2
+ wα α t − ˆ α t( )2
i=1
m
∑
Motion:
Pose:
Key Pose Detection
4 key poses: