Vehicle type classification based on frontal profile

Abstract

Vehicle recognition and classification is useful to many traffic management activities and is one of the important technologies in Intelligent Transportation System (ITS). Automatic toll collection, vehicle access control, traffic forecast and volume, congestion management and speed monitoring are some of the systems that have a need to classify the vehicle types. This project highlights the potential use of vehicle dimensions and shape information for the vehicle type classification. The system profiles the frontal view of the vehicles and extracts the basic outline shapes and geometrical parameters and compares against the stored specification for each class. The design utilizes Hough line detection algorithm to identify the location of vehicle properties that needs to be extracted. Input to the system is a static image of vehicle frontal view. The image is processed through various image processing methods namely; median filtering, morphological reconstruction, edge detection and Hough transform. Finally, the designed system is able to determine and classify various vehicle types into three major classes; car, bus and truck. This design is a useful support system to existing Plate Number Recognition System (PNRS) which will help to increase the accuracy of the results obtained.