Robust people tracking and SHMM learning using SHMMs

Sehestedt, S; Kodagoda, S; Dissanayake, G

Robust people tracking and SHMM learning using SHMMs

Sehestedt, S Kodagoda, S

Dissanayake, G

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2011 Australasian Conference on Robotics and Automation, 2011
Issue Date:: 2011-12-01

Closed Access

	Filename	Description	Size
	2010006654OK.pdf	Published version	3.45 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Sehestedt, S	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings of the 2011 Australasian Conference on Robotics and Automation, 2011	en_US
dc.identifier.isbn	9780980740424	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32360
dc.description.abstract	For effective Human Robot Interaction (HRI), it is necessary for the robot to be aware of its human peers and be able to anticipate and predict their actions. This paper explores an improved strategy for people tracking using Sampled Hidden Markov Models (SHMM) for capturing common human motion patterns. Such an SHMM contains rich information about human spatial behavior and it can be learned online during robot operation. The proposed integration of people tracking and learning offers significant improvements to the outcomes when compared to existing techniques. Real world experiments that demonstrate the viability and effectiveness of the approach are presented.	en_US
dc.relation.ispartof	Proceedings of the 2011 Australasian Conference on Robotics and Automation	en_US
dc.title	Robust people tracking and SHMM learning using SHMMs	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Melbourne
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

For effective Human Robot Interaction (HRI), it is necessary for the robot to be aware of its human peers and be able to anticipate and predict their actions. This paper explores an improved strategy for people tracking using Sampled Hidden Markov Models (SHMM) for capturing common human motion patterns. Such an SHMM contains rich information about human spatial behavior and it can be learned online during robot operation. The proposed integration of people tracking and learning offers significant improvements to the outcomes when compared to existing techniques. Real world experiments that demonstrate the viability and effectiveness of the approach are presented.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/32360