| Abstract |
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We investigate the problem of learning the structure of an
articulated object, i.e. its kinematic chain, from feature trajectories
under affine projections. We demonstrate this possibility
by proposing an algorithm which first segments the
trajectories by local sampling and spectral clustering, then
builds the kinematic chain as a minimum spanning tree of
a graph constructed from the segmented motion subspaces.
We test our method in challenging data sets and demonstrate
the ability to automatically build the kinematic chain
of an articulated object from feature trajectories. The algorithm
also works when there are multiple articulated objects
in the scene. Furthermore, we take into account non-rigid
articulated parts that exist in human motions. We believe
this advance will have impact on articulated object tracking
and dynamical structure from motion.
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 Additional Information
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Citation:
Jingyu Yan, Marc Pollefeys,
"Automatic Kinematic Chain Building from Feature Trajectories of Articulated Objects,"
cvpr,
pp. 712-719,
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06),
2006
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