Gao, JiangShi, Jianbo2023-05-222023-05-222004-05-172004-11-11https://repository.upenn.edu/handle/20.500.14332/6380We 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.Bayes methodsMarkov processesbelief networksgraph theoryimage motion analysisobject detectiontrackingBayesian inferenceMarkov network modelbelief propagationgraph modelhuman motion detectionhuman motion trackinghuman upper body motionmotion energy imagemultiple frame motion inference modelPresentation