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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.
Bayes methods, Markov processes, belief networks, graph theory, image motion analysis, object detection, tracking, Bayesian inference, Markov network model, belief propagation, graph model, human motion detection, human motion tracking, human upper body motion, motion energy image, multiple frame motion inference model
Date Posted: 15 November 2004
This document has been peer reviewed.