C. Sminchisescu and A. Telea
Abstract
We present a novel similarity measure (likelihood) for estimating
three-dimensional human pose from image silhouettes in model-based
vision
applications. One of the challenges in
such approaches is the construction of a model-to-image likelihood
that
truly reflects the good configurations of the problem.
This is hard, commonly due to the violation of consistency principle
resulting in the
introduction of spurious, unrelated peaks/minima that make the search
for model localization difficult. We introduce an entirely continuous
formulation which enforces model estimation consistency by means of
an
attraction/explanation silhouette-based term pair. We subsequently
show how the
proposed method provides significant consolidation and improved attraction
zone
around the desired likelihood configurations and elimination of some
of the spurious ones.
Finally, we present a skeleton-based smoothing method for the image
silhouettes that stabilizes and accelerates the search process.
Keywords human tracking, model-based estimation, constrained
optimization,
level set methods, fast marching methods