Automatic vehicle classification program using image processing for electronic tollway systems

This thesis is aimed at developing an Automatic Vehicle Classification (AVC) System designed to aid and improve on the existing Computerized Toll Collection (CTC) System. Human errors are avoided by automating the vehicle classification process, and thus helps the toll companies minimize, if not eli...

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Main Authors: Nunez, Kristina D., Santiago, Jose Carlo C., Villamayor, Renato Q., Yu Ping Kun, Desiree D.
Format: text
Language:English
Published: Animo Repository 2001
Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/11695
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-123402021-09-02T02:56:44Z Automatic vehicle classification program using image processing for electronic tollway systems Nunez, Kristina D. Santiago, Jose Carlo C. Villamayor, Renato Q. Yu Ping Kun, Desiree D. This thesis is aimed at developing an Automatic Vehicle Classification (AVC) System designed to aid and improve on the existing Computerized Toll Collection (CTC) System. Human errors are avoided by automating the vehicle classification process, and thus helps the toll companies minimize, if not eliminate, deficits. AVC Systems depend mainly on image processing. The system takes a digital picture of the vehicle on the lane, processes this information, and determines whether the vehicle is categorized as Class 1 (cars, jeeps, pick-up and vans), Class 2 (buses, small trucks and Class 1 vehicles with 1-axle or 2-axle tracks) or Class 3 (large to long haul trucks). Along with the development of the AVC System is the design of a CTC System that is similar to that developed by Micrologic Inc. The AVC System is integrated into the CTC System to replace the manual input of the vehicle class that is the original task of the toolbooth operator. A further enhancement such as network security in the form of file encryption is also developed. Preliminary testing was conducted using images of actual and small-scale vehicles captured outdoors. Data was gathered using these captured images to determine the criteria for detecting each vehicle class. Actual demonstration, however, is performed using small-scale vehicles as subjects. Since the image-processing program is highly sensitive to ambient light variations and the actual color of the vehicle, errors are likely to occur in the detection process. However, these errors are kept within acceptable limits through the introduction of external factors such as proper lighting and roofing, thus ensuring the reliability of the system at all times of the day. 2001-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/11695 Bachelor's Theses English Animo Repository
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
description This thesis is aimed at developing an Automatic Vehicle Classification (AVC) System designed to aid and improve on the existing Computerized Toll Collection (CTC) System. Human errors are avoided by automating the vehicle classification process, and thus helps the toll companies minimize, if not eliminate, deficits. AVC Systems depend mainly on image processing. The system takes a digital picture of the vehicle on the lane, processes this information, and determines whether the vehicle is categorized as Class 1 (cars, jeeps, pick-up and vans), Class 2 (buses, small trucks and Class 1 vehicles with 1-axle or 2-axle tracks) or Class 3 (large to long haul trucks). Along with the development of the AVC System is the design of a CTC System that is similar to that developed by Micrologic Inc. The AVC System is integrated into the CTC System to replace the manual input of the vehicle class that is the original task of the toolbooth operator. A further enhancement such as network security in the form of file encryption is also developed. Preliminary testing was conducted using images of actual and small-scale vehicles captured outdoors. Data was gathered using these captured images to determine the criteria for detecting each vehicle class. Actual demonstration, however, is performed using small-scale vehicles as subjects. Since the image-processing program is highly sensitive to ambient light variations and the actual color of the vehicle, errors are likely to occur in the detection process. However, these errors are kept within acceptable limits through the introduction of external factors such as proper lighting and roofing, thus ensuring the reliability of the system at all times of the day.
format text
author Nunez, Kristina D.
Santiago, Jose Carlo C.
Villamayor, Renato Q.
Yu Ping Kun, Desiree D.
spellingShingle Nunez, Kristina D.
Santiago, Jose Carlo C.
Villamayor, Renato Q.
Yu Ping Kun, Desiree D.
Automatic vehicle classification program using image processing for electronic tollway systems
author_facet Nunez, Kristina D.
Santiago, Jose Carlo C.
Villamayor, Renato Q.
Yu Ping Kun, Desiree D.
author_sort Nunez, Kristina D.
title Automatic vehicle classification program using image processing for electronic tollway systems
title_short Automatic vehicle classification program using image processing for electronic tollway systems
title_full Automatic vehicle classification program using image processing for electronic tollway systems
title_fullStr Automatic vehicle classification program using image processing for electronic tollway systems
title_full_unstemmed Automatic vehicle classification program using image processing for electronic tollway systems
title_sort automatic vehicle classification program using image processing for electronic tollway systems
publisher Animo Repository
publishDate 2001
url https://animorepository.dlsu.edu.ph/etd_bachelors/11695
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