Multi-scale vehicle classification using different machine learning models

• Main Authors: Roxas, Edison A., Vicerra, Ryan Rhay P., Gan Lim, Laurence A., Dela Cruz, Jennifer C., Naguib, Raouf N. G., Dadios, Elmer Jose P., Bandala, Argel A.
• Format: text
• Published: Animo Repository 2019
• Subjects: Vehicles; Computer vision; Machine learning; Nearest neighbor analysis (Statistics); Kernel functions; Logistic regression analysis; Manufacturing; Mechanical Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2934
• Institution: De La Salle University

The focus of this paper is to explore multi-scale vehicle classification based on the histogram of oriented gradient features. Several literatures have used these features together with different classification models, however, there is a need to compare different models suited for vehicle classification application. In order to quantify the results a common dataset was used for the machine learning models: logistic regression, k-nearest neighbor, and support vector machine. However, since the classification of the support vector machine is based on the type of kernel (linear, polynomial, and Gaussian) used, additional tests were conducted. Thus, this study provides the following contributions: (1) comparison of machine learning models for vehicle classification; and (2) comparison of the best type of kernel function. © 2018 IEEE.