Affordance-map: Mapping human context in 3D scenes using cost-sensitive SVM and virtual human models

Piyathilaka, L; Kodagoda, S

Affordance-map: Mapping human context in 3D scenes using cost-sensitive SVM and virtual human models

Piyathilaka, L

Kodagoda, S

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Publication Type:: Conference Proceeding
Citation:: 2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015, 2015, pp. 2035 - 2040
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Piyathilaka, L https://orcid.org/0000-0003-4676-9387	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015, 2015, pp. 2035 - 2040	en_US
dc.identifier.isbn	9781467396745	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126029
dc.description.abstract	© 2015 IEEE. Robots are often required to operate in environments where humans are not present, but yet require the human context information for better human robot interaction. Even when humans are present in the environment, detecting their presence in cluttered environments could be challenging. As a solution to this problem, this paper presents the concept of affordance-map which learns human context by looking at geometric features of the environment. Instead of observing real humans to learn human context, it uses virtual human models and their relationships with the environment to map hidden human affordances in 3D scenes. The affordance-map learning problem is formulated as a multi label classification problem that can be learned using cost-sensitive SVM. Experiments carried out in a real 3D scene dataset recorded promising results and proved the applicability of affordance-map for mapping human context.	en_US
dc.relation.ispartof	2015 IEEE International Conference on Robotics and Biomimetics, IEEE-ROBIO 2015	en_US
dc.relation.isbasedon	10.1109/ROBIO.2015.7419073	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Affordance-map: Mapping human context in 3D scenes using cost-sensitive SVM and virtual human models	en_US
dc.type	Conference Proceeding
utslib.for	091303 Autonomous Vehicles	en_US
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	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. Robots are often required to operate in environments where humans are not present, but yet require the human context information for better human robot interaction. Even when humans are present in the environment, detecting their presence in cluttered environments could be challenging. As a solution to this problem, this paper presents the concept of affordance-map which learns human context by looking at geometric features of the environment. Instead of observing real humans to learn human context, it uses virtual human models and their relationships with the environment to map hidden human affordances in 3D scenes. The affordance-map learning problem is formulated as a multi label classification problem that can be learned using cost-sensitive SVM. Experiments carried out in a real 3D scene dataset recorded promising results and proved the applicability of affordance-map for mapping human context.

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http://hdl.handle.net/10453/126029