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 Proceedings of the 2008 4th International Conference on Information and Automation for Sustainability, IEEE, Piscataway, N.J., pp. 349-360, doi: 10.1109/ICIAFS.2008.4783980.

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Title Visual tracking of vehicles using multiresolution analysis and neural network
Author(s) Fernando, Shehan
Udawatta, Lanka
Pathirana, PubuduORCID iD for Pathirana, Pubudu
Conference name Information and Automation for Sustainability. Conference (4th : 2008 Colombo, Sri Lanka)
Conference location Colombo, Sri Lanka
Conference dates 12-14 Dec. 2008
Title of proceedings Proceedings of the 2008 4th International Conference on Information and Automation for Sustainability
Publication date 2008
Start page 349
End page 360
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Automation & Control Systems
Computer Science, Hardware & Architecture
Engineering, Electrical & Electronic
Computer Science
multiresolution analysis
haar transform
principle component analysis
eigen image
multilayer feedforward neural network
Summary This paper describes the procedure for detectionand tracking of a vehicle from an on-road image sequence takenby a monocular video capturing device in real time. The mainobjective of such a visual tracking system is to closely followobjects in each frame of a video stream, such that the objectposition as well as other geometric information are alwaysknown. In the tracking system described, the video capturingdevice is also moving. It is a challenge to detect and track amoving vehicle under a constantly changing environment coupledto real time video processing. The system suggested is robust toimplement under different illuminating conditions by using themonocular video capturing device. The vehicle trackingalgorithm is one of the most important modules in anautonomous vehicle system, not only it should be very accuratebut also must have the safety of other vehicles, pedestrians, andthe moving vehicle itself. In order to achieve this an algorithm ofmulti resolution technique based on Haar basis functions wereused for the wavelet transform, where a combination ofclassification was carried out with the multilayer feed forwardneural network. The classification is done in a reduceddimensional space, where Principle Component Analysis (PCA)dimensional reduction technique has been applied to make theclassification process much more efficient. The results show theeffectiveness of the proposed methodology
ISBN 9781424428991
Language eng
DOI 10.1109/ICIAFS.2008.4783980
Field of Research 090299 Automotive Engineering not elsewhere classified
Socio Economic Objective 0 Not Applicable
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2008, IEEE
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