3D human motion tracking has received increasing attention in recent years due to its
broad applications.Among various 3D human motion tracking methods,the particle filter
is regarded as one of the most effective algorithms.However,there are still several
limitations of current particle filter approaches such as low prediction accuracy and
sensitivity to discontinuous motion caused by low fram erateorsuddenchangeofhuman
motion velocity.Targetingsuchproblems,this paperp resentsafull-body human motion
trackingsystembyproposingexemplar-basedconditionalparticlefilter(EC-PF)for
monocularcamera.Byintroducingaconditionaltermwithrespecttoexemplarsand
imagedata,dynamicmodelisapproximatedandusedtopredictcurrentstatesofparticles
in predictionphase.Inupdatephase,weightsofparticlesarerefinedbymatchingimages
withprojectedhumanmodelusingasetoffeatures.