Deakin University

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Visual tracking of vehicles using multiresolution analysis and neural network

conference contribution
posted on 2008-01-01, 00:00 authored by S Fernando, L Udawatta, Pubudu PathiranaPubudu Pathirana
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.



IEEE International Conference on Information and Automation for Sustainability (4th : 2008 : Colombo, Sri Lanka)


355 - 360




Colombo, Sri Lanka

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E1 Full written paper - refereed

Copyright notice

2008, IEEE

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