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Pedestrian detection for mobile bus surveillance

Leoputra, Wilson S., Venkatesh, Svetha and Tan, Tele 2008, Pedestrian detection for mobile bus surveillance, in ICARCV 2008 : Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision, IEEE, [Washington, D. C.], pp. 726-732.

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Title Pedestrian detection for mobile bus surveillance
Author(s) Leoputra, Wilson S.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Tan, Tele
Conference name International Conference on Control, Automation, Robotics and Vision (10th : 2008 : Hanoi, Vietnam)
Conference location Hanoi, Vietnam
Conference dates 17-20 Dec. 2008
Title of proceedings ICARCV 2008 : Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision
Editor(s) [Unknown]
Publication date 2008
Conference series International Conference on Control, Automation, Robotics and Vision
Start page 726
End page 732
Total pages 7
Publisher IEEE
Place of publication [Washington, D. C.]
Keyword(s) hierarchical template matching
homography
non-parametric background modeling
scene localization
Summary In this paper, we present a system for pedestrian detection involving scenes captured by mobile bus surveillance cameras in busy city streets. Our approach integrates scene localization, foreground and background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data. In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarities and second stage further clusters these aligned frames in terms of lighting. This produces clusters of images which are differential in viewpoint and lighting. A kernel density estimation (KDE) method for colour and gradient foreground-background separation are then used to construct background model for each image cluster which is subsequently used to detect all foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be identified. We have tested our system on a set of real bus video datasets and the experimental results verify that our system works well in practice.
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 1424422868
9781424422869
Language eng
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 ©2008, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044574

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.