VEHICLE DETECTION AND TYPE CLASSIFICATION USING ORB - RANSAC - RIZQI SHOLAHUDDIN (NIM : 23514074), MUHAMMAD
Detection and type classification of vehicles are part of Intelligent Transportation System. This paper proposed system for detection and type classification of vehicles based on Oriented FAST and Rotated BRIEF (ORB) method to extract local features in vehicles image, hamming distance for matching local features and RANSAC to eliminate matching errors. Evaluation of performance used real vehicle images taken from multiple viewpoints and sizes of google street view. The vehicles are categorized into 5 classes: trucks, buses, sedans, pick-ups, and mpv/suv/van. The results show that the accuracy from 717 test images is 94.28%. From the results, it can be concluded that the use of ORB, hamming distance, and RANSAC algorithm are able to categorize the vehicles more specifically and robustly with changes in viewpoint and size.