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Visual tracking of vehicles using multiresolution analysis and neural network

Fernando, Shehan, Udawatta, Lanka and Pathirana, Pubudu 2008, Visual tracking of vehicles using multiresolution analysis and neural network, in ICIAFS 2008 : Sustainable development through effective man-machine co-existence : Proceedings of the 4th International Conference on Information and Automation for Sustainability, IEEE, Piscataway, N.J., pp. 355-360.

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Title Visual tracking of vehicles using multiresolution analysis and neural network
Author(s) Fernando, Shehan
Udawatta, Lanka
Pathirana, Pubudu
Conference name IEEE International Conference on Information and Automation for Sustainability (4th : 2008 : Colombo, Sri Lanka)
Conference location Colombo, Sri Lanka
Conference dates 12-14 December 2008
Title of proceedings ICIAFS 2008 : Sustainable development through effective man-machine co-existence : Proceedings of the 4th International Conference on Information and Automation for Sustainability
Editor(s) [Unknown]
Publication date 2008
Conference series International Conference on Information and Automation for Sustainability
Start page 355
End page 360
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) eigen image
haar transform
multilayer feedforward neural network
multiresolution analysis
principle component analysis
Summary This paper describes the procedure for detection and tracking of a vehicle from an on-road image sequence taken by a monocular video capturing device in real time. The main objective of such a visual tracking system is to closely follow objects in each frame of a video stream, such that the object position as well as other geometric information are always known. In the tracking system described, the video capturing device is also moving. It is a challenge to detect and track a moving vehicle under a constantly changing environment coupled to real time video processing. The system suggested is robust to implement under different illuminating conditions by using the monocular video capturing device. The vehicle tracking algorithm is one of the most important modules in an autonomous vehicle system, not only it should be very accurate but also must have the safety of other vehicles, pedestrians, and the moving vehicle itself. In order to achieve this an algorithm of multi resolution technique based on Haar basis functions were used for the wavelet transform, where a combination of classification was carried out with the multilayer feed forward neural network. The classification is done in a reduced dimensional space, where principle component analysis (PCA) dimensional reduction technique has been applied to make the classification process much more efficient. The results show the effectiveness of the proposed methodology.
ISBN 9781424429004
Language eng
Field of Research 090602 Control Systems
Socio Economic Objective 861799 Communication Equipment not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30018204

Document type: Conference Paper
Collection: School of Engineering
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Created: Fri, 14 Aug 2009, 14:05:59 EST