Learning articulated motion structures with Bayesian Networks
Ramos, Fabio. T, Durrant-Whyte, Hugh. F, & Upcroft, Ben (2005) Learning articulated motion structures with Bayesian Networks. In Proceedings 8th International Conference on Information Fusion, 2005, IEEE, Wyndham Philadelphia at Franklin Plaza Philadelphia, PA, USA.
This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.
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|Item Type:||Conference Paper|
|Keywords:||Bayesian network, nonlinear correlation, moving object classification, image feature extraction, human gait analysis, articulated motion structure learning, cluster feature|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2005 IEEE|
|Copyright Statement:||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 to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Deposited On:||09 May 2011 15:03|
|Last Modified:||14 Aug 2011 00:21|
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