J. Zhang, R. Collins, and Y. Liu,
"Bayesian Body Localization Using Mixture of Nonlinear Shape Models,"
International Conference on Computer Vision (ICCV'05),
Beijing China, October 2005, pp.725-732.
We present a 2D model-based approach to localizing human body in
images viewed from arbitrary and unknown angles. The central component
is a statistical shape representation of the nonrigid and articulated
body contours, where a non-linear deformation is decomposed based on
the concept of parts. Several image cues are combined to relate the
body configuration to the observed image, with self-occlusion
explicitly treated. To accommodate large viewpoint changes, a mixture of
view-dependent models is employed. Inference is done by direct
sampling of the posterior mixture, using Sequential Monte Carlo (SMC)
simulation enhanced with annealing and kernel move. The fitting method
is independent of the number of mixture components, and does not
require the preselection of a "correct" viewpoint. The models were
trained on a large number of interactively labeled gait images.
Preliminary tests demonstrated the feasibility of the proposed
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