Image-based Vehicle Classification System
This is an incremental improvement for toll collection systems in Malaysia, aiming to reduce costs and support migration to multi-lane free flow.
The paper tackled vehicle classification for electronic toll collection by developing an image-based system using SIFT, Canny's edge detector, K-means clustering, and Euclidean distance matching, achieving promising results on open datasets.
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.