Vehicle classification using AKAZE and feature matching approach and artificial neural network

• Main Authors: Bedruz, Rhen Anjerome R., Fernando, Arvin, Bandala, Argel A., Sybingco, Edwin, Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2019
• Subjects: Computer vision; Image registration; Image processing—Digital techniques; Neural networks (Computer science); Electrical and Computer Engineering; Electrical and Electronics; Systems and Communications
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1540; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2539/type/native/viewcontent
• Institution: De La Salle University

This research proposes a method in order to classify vehicles in a highly congested roads , a robust technique for vehicle classification with low computational power must be used. So, a proposed solution is to embed an AKAZE feature matching extraction which is ran in an artificial neural network will be used. AKAZE was used because it is faster than SIFT. The features extracted from the AKAZE algorithm will be grouped according to the type of vehicle where it was used and be placed to an Artificial Neural Network (ANN) for the training of the network itself. The results yielded good for real-time Vehicle Classification. © 2018 IEEE.