Abstract

   We present an algorithm for automatic inference of
human upper body motion. A graph model is proposed
for inferring human motion, and motion inference is
posed as a mapping problem between state nodes in
the graph model and features in image patches. Belief
propagation is utilized for Bayesian inference in this
graph. A multiple-frame inference model/algorithm is
proposed to combine both structural and temporal
constraints in human motion. We also present a
method for capturing constraints of human body
configuration under different view angles. The
algorithm is applied in a prototype system that can
automatically label upper body motion from videos,
without manual initialization of body parts.