Classification of Motorcycles using Extracted Images of Traffic Monitoring Videos

TL;DR

This paper develops a motorcycle classification system using image features from traffic videos, achieving over 90% accuracy in distinguishing motorcycles from non-motorcycles.

Contribution

It introduces a new motorcycle image database and combines LBP features with LinearSVC for effective vehicle classification in traffic monitoring.

Findings

Achieved over 90% accuracy in classification.

Created a comprehensive motorcycle image bank.

Demonstrated the effectiveness of LBP and LinearSVC combination.

Abstract

Due to the great growth of motorcycles in the urban fleet and the growth of the study on its behavior and of how this vehicle affects the flow of traffic becomes necessary the development of tools and techniques different from the conventional ones to identify its presence in the traffic flow and be able to extract your information. The article in question attempts to contribute to the study on this type of vehicle by generating a motorcycle image bank and developing and calibrating a motorcycle classifier by combining the LBP techniques to create the characteristic vectors and the classification technique LinearSVC to perform the predictions. In this way the classifier of vehicles of the type motorcycle developed in this research can classify the images of vehicles extracted of videos of monitoring between two classes motorcycles and non-motorcycles with a precision and an accuracy superior to 0,9.