3D human motion tracking has received increasing attention in recent years due to its
broad applications Among various 3D human motiont tracking methods, the particle filter
is regarded as one of the most effective algorithms However,there are still several
limitations of current particle filter approachess uch as low prediction accuracy and
ensitivity todiscontinuous motion caused by low frame rate or sudden change of human
motion velocity.Targeting such problems,this paper presents a full-body human motion
tracking system by proposing exemplar-based conditional particle filter(EC-PF) for
monocular camera.By introducing a conditional term with respect to exemplars and
image data,dynamic model is approximated and used to predict current states of particles
in prediction phase.In update phase,weights of particles are refined by matching images
with projected human model using a set of features.