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