Multiple Frame Motion Inference Using Belief Propagation
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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
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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.
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Copyright 2004 IEEE. Reprinted from Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition 2004 (FGR 2004), pages 875-880. Publisher URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumb er=28919&page=9 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.