  Electronic toll collection (ETC) system has been a common trend used for toll
collection on toll road nowadays. The implementation of electronic toll
collection allows vehicles to travel at low or full speed during the toll
payment, which help to avoid the traffic delay at toll road. One of the major
components of an electronic toll collection is the automatic vehicle detection
and classification (AVDC) system which is important to classify the vehicle so
that the toll is charged according to the vehicle classes. Vision-based vehicle
classification system is one type of vehicle classification system which adopt
camera as the input sensing device for the system. This type of system has
advantage over the rest for it is cost efficient as low cost camera is used.
The implementation of vision-based vehicle classification system requires lower
initial investment cost and very suitable for the toll collection trend
migration in Malaysia from single ETC system to full-scale multi-lane free flow
(MLFF). This project includes the development of an image-based vehicle
classification system as an effort to seek for a robust vision-based vehicle
classification system. The techniques used in the system include
scale-invariant feature transform (SIFT) technique, Canny's edge detector,
K-means clustering as well as Euclidean distance matching. In this project, a
unique way to image description as matching medium is proposed. This
distinctiveness of method is analogous to the human DNA concept which is highly
unique. The system is evaluated on open datasets and return promising results.

Computer Vision and Pattern Recognition