Vehicle classification using machine learning algorithm

<p> Increasing traffic on roadways requires some real-time system that can collect traffic data and helps us to manage existing road infrastructure. For this purpose, we need a state of art system that can detect and classify vehicles into different categories. We developed an in-node micropro...

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Main Author: Patel, Darshan D.
Language:EN
Published: California State University, Long Beach 2015
Subjects:
Online Access:http://pqdtopen.proquest.com/#viewpdf?dispub=1604876
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spelling ndltd-PROQUEST-oai-pqdtoai.proquest.com-16048762015-12-17T03:57:57Z Vehicle classification using machine learning algorithm Patel, Darshan D. Electrical engineering <p> Increasing traffic on roadways requires some real-time system that can collect traffic data and helps us to manage existing road infrastructure. For this purpose, we need a state of art system that can detect and classify vehicles into different categories. We developed an in-node microprocessor-based vehicle classification system to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm, which is implemented in machine learning software Weka. J48 is a Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The generated tree model can then be easily implemented on microprocessors. The result of our experiment shows that the vehicle classification system is effective and efficient with the very high accuracy at ~98%. </p> California State University, Long Beach 2015-12-11 00:00:00.0 thesis http://pqdtopen.proquest.com/#viewpdf?dispub=1604876 EN
collection NDLTD
language EN
sources NDLTD
topic Electrical engineering
spellingShingle Electrical engineering
Patel, Darshan D.
Vehicle classification using machine learning algorithm
description <p> Increasing traffic on roadways requires some real-time system that can collect traffic data and helps us to manage existing road infrastructure. For this purpose, we need a state of art system that can detect and classify vehicles into different categories. We developed an in-node microprocessor-based vehicle classification system to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Our approach for vehicle classification utilizes J48 classification algorithm, which is implemented in machine learning software Weka. J48 is a Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The generated tree model can then be easily implemented on microprocessors. The result of our experiment shows that the vehicle classification system is effective and efficient with the very high accuracy at ~98%. </p>
author Patel, Darshan D.
author_facet Patel, Darshan D.
author_sort Patel, Darshan D.
title Vehicle classification using machine learning algorithm
title_short Vehicle classification using machine learning algorithm
title_full Vehicle classification using machine learning algorithm
title_fullStr Vehicle classification using machine learning algorithm
title_full_unstemmed Vehicle classification using machine learning algorithm
title_sort vehicle classification using machine learning algorithm
publisher California State University, Long Beach
publishDate 2015
url http://pqdtopen.proquest.com/#viewpdf?dispub=1604876
work_keys_str_mv AT pateldarshand vehicleclassificationusingmachinelearningalgorithm
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