Robust and efficient people detection with 3-D range data using shape matching

Hordern, D; Kirchner, N

Robust and efficient people detection with 3-D range data using shape matching

Hordern, D Kirchner, N

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010, 2010
Issue Date:: 2010-12-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (1.56 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Hordern, D	en_US
dc.contributor.author	Kirchner, N https://orcid.org/0000-0002-3018-992X	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010, 2010	en_US
dc.identifier.isbn	9780980740417	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16431
dc.description.abstract	Information about the location of a person is a necessity for Human-Robot Interaction (HRI) as it enables the robot to make human aware decisions and facilitates the extraction of further useful information; such as low-level gestures and gaze. This paper presents a robust method for person detection with 3-D range data using shape matching. Projections of the 3-D data onto 2-D planes are exploited to effectively and efficiently represent the data for scene segmentation and shape extraction. Fourier descriptors (FD) are used to describe the shapes and are subsequently classified with a Support Vector Machine (SVM). A database of 25 people was collected and used to test this approach. The results show that the computationally efficient shape features can be used to robustly detect the location of people.	en_US
dc.relation.ispartof	Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010	en_US
dc.title	Robust and efficient people detection with 3-D range data using shape matching	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Brisbane, Queensland, Australia	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
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
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

Information about the location of a person is a necessity for Human-Robot Interaction (HRI) as it enables the robot to make human aware decisions and facilitates the extraction of further useful information; such as low-level gestures and gaze. This paper presents a robust method for person detection with 3-D range data using shape matching. Projections of the 3-D data onto 2-D planes are exploited to effectively and efficiently represent the data for scene segmentation and shape extraction. Fourier descriptors (FD) are used to describe the shapes and are subsequently classified with a Support Vector Machine (SVM). A database of 25 people was collected and used to test this approach. The results show that the computationally efficient shape features can be used to robustly detect the location of people.

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

http://hdl.handle.net/10453/16431