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Combining image regions and human activity for indirect object recognition in indoor wide-angle views

Peursum, Patrick, West, Geoff and Venkatesh, Svetha 2005, Combining image regions and human activity for indirect object recognition in indoor wide-angle views, in ICCV 2005 : Proceedings of the IEEE International Conference on Computer Vision, IEEE, Los Alamitos, Calif., pp. 82-89, doi: 10.1109/ICCV.2005.57.

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Title Combining image regions and human activity for indirect object recognition in indoor wide-angle views
Author(s) Peursum, Patrick
West, Geoff
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name IEEE International Conference on Computer Vision (10th : 2005 : Beijing, China)
Conference location Beijing, China
Conference dates 17-20 Oct. 2005
Title of proceedings ICCV 2005 : Proceedings of the IEEE International Conference on Computer Vision
Editor(s) [Unknown]
Publication date 2005
Conference series International Conference on Computer Vision
Start page 82
End page 89
Total pages 8
Publisher IEEE
Place of publication Los Alamitos, Calif.
Keyword(s) Bayesian methods
computer vision
humans
layout
object recognition
shape
smart homes
surveillance
Summary Traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wideangle and low-detail views such as surveillance scenes. To address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. No shape analysis is necessary. The concept is dubbed 'interaction signatures', since the premise is that a human will interact with objects in ways characteristic of the function of that object - for example, a person sits in a chair and drinks from a cup. The human-centred approach means that recognition is possible in low-detail views and is largely invariant to the shape of objects within the same functional class. This paper implements a Bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human's interactions with the objects. Experiments show that interaction signatures can successfully find and label objects in low-detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 9780769523347
076952334X
ISSN 1550-5499
Language eng
DOI 10.1109/ICCV.2005.57
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2005, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044614

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.