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.

View at publisher


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.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

69 since deposited on 09 May 2011
3 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 40427
Item Type: Conference Paper
Refereed: No
Keywords: Bayesian network, nonlinear correlation, moving object classification, image feature extraction, human gait analysis, articulated motion structure learning, cluster feature
DOI: 10.1109/ICIF.2005.1591927
ISBN: 0780392868
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 05:03
Last Modified: 13 Aug 2011 14:21

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page