Visual tracking of vehicles using multiresolution analysis and neural network

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