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Body parts segmentation with attached props using RGB-D imaging

Haggag, H., Hossny, M., Nahavandi, S., Haggag, S. and Creighton, D. 2015, Body parts segmentation with attached props using RGB-D imaging, in DICTA 2015 : Proceedings of the Digital Image Computing: Techniques and Applications 2015 International Conference, IEEE, Piscataway, N. J., pp. 1-8, doi: 10.1109/DICTA.2015.7371243.

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Title Body parts segmentation with attached props using RGB-D imaging
Author(s) Haggag, H.
Hossny, M.ORCID iD for Hossny, M. orcid.org/0000-0002-1593-6296
Nahavandi, S.
Haggag, S.
Creighton, D.ORCID iD for Creighton, D. orcid.org/0000-0002-9217-1231
Conference name Digital Image Computing Techniques and Applications. International Conference (2015 : Adelaide, S.A.)
Conference location Adelaide, S.A.
Conference dates 23-25 Nov. 2015
Title of proceedings DICTA 2015 : Proceedings of the Digital Image Computing: Techniques and Applications 2015 International Conference
Editor(s) [Unknown]
Publication date 2015
Conference series Digital Image Computing Techniques and Applications International Conference
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) Kinect
Depth sensors
RGB-D cameras
Summary People detection is essential in a lot of different systems. Many applications nowadays tend to require people detection to achieve certain tasks. These applications come under many disciplines, such as robotics, ergonomics, biomechanics, gaming and automotive industries. This wide range of applications makes human body detection an active area of research. With the release of depth sensors or RGB-D cameras such as Micosoft Kinect, this area of research became more active, specially with their affordable price. Human body detection requires the adaptation of many scenarios and situations. Various conditions such as occlusions, background cluttering and props attached to the human body require training on custom built datasets. In this paper we present an approach to prepare training datasets to detect and track human body with attached props. The proposed approach uses rigid body physics simulation to create and animate different props attached to the human body. Three scenarios are implemented. In the first scenario the prop is closely attached to the human body, such as a person carrying a backpack. In the second scenario, the prop is slightly attached to the human body, such as a person carrying a briefcase. In the third scenario the prop is not attached to the human body, such as a person dragging a trolley bag. Our approach gives results with accuracy of 93% in identifying both the human body parts and the attached prop in all the three scenarios.
ISBN 9781467367950
Language eng
DOI 10.1109/DICTA.2015.7371243
Field of Research 080106 Image Processing
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084920

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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