Vision-Based Intelligent System for Traffic Analysis (VISTA)

Problems with existing traffic surveillance and detection systems have led researchers to venture into the use of computer vision technology in traffic analysis. In order to be implemented in the Philippines, however, additional parameters need to be considered such as various modes of transportatio...

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Main Authors: Lai, Francis P., Leong, Alvin Cedric Y., Ortuoste, Ricardo L., Yu, Paul K.
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Language:English
Published: Animo Repository 2008
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Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/14598
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-152402021-11-11T02:23:11Z Vision-Based Intelligent System for Traffic Analysis (VISTA) Lai, Francis P. Leong, Alvin Cedric Y. Ortuoste, Ricardo L. Yu, Paul K. Problems with existing traffic surveillance and detection systems have led researchers to venture into the use of computer vision technology in traffic analysis. In order to be implemented in the Philippines, however, additional parameters need to be considered such as various modes of transportation (i.e. bicycles, pedicabs, calesas), and some non-vehicle contributions to traffic, which include processions and rallies. In light of this, VISTA endeavors to implement a multiple vehicle tracking and traffic analysis system based on Philippine roadways. The study focuses on implementing a data acquisition system necessary for traffic analysis. The input of the system is an image sequence. Each image is then processed to identify entities on the road that contribute to traffic. Once these entities are identified, traffic density and traffic flow are then computed based on the image sequence. The output of the system can be used as the necessary traffic parameters for traffic management and information systems. The system is capable of detecting global brightness drops which are treated as shadows. The system can process at 3.22 frames per second for background modeling, and at 2.47 frames per second for result generation. Traffic density and traffic flow are directly influenced by the accuracy of the detection scheme. The accuracy of detection is 68.75% and is heavily dependent on how the system is calibrated. 2008-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/14598 Bachelor's Theses English Animo Repository Traffic engineering Traffic flow--Computer simulation Computer vision
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
topic Traffic engineering
Traffic flow--Computer simulation
Computer vision
spellingShingle Traffic engineering
Traffic flow--Computer simulation
Computer vision
Lai, Francis P.
Leong, Alvin Cedric Y.
Ortuoste, Ricardo L.
Yu, Paul K.
Vision-Based Intelligent System for Traffic Analysis (VISTA)
description Problems with existing traffic surveillance and detection systems have led researchers to venture into the use of computer vision technology in traffic analysis. In order to be implemented in the Philippines, however, additional parameters need to be considered such as various modes of transportation (i.e. bicycles, pedicabs, calesas), and some non-vehicle contributions to traffic, which include processions and rallies. In light of this, VISTA endeavors to implement a multiple vehicle tracking and traffic analysis system based on Philippine roadways. The study focuses on implementing a data acquisition system necessary for traffic analysis. The input of the system is an image sequence. Each image is then processed to identify entities on the road that contribute to traffic. Once these entities are identified, traffic density and traffic flow are then computed based on the image sequence. The output of the system can be used as the necessary traffic parameters for traffic management and information systems. The system is capable of detecting global brightness drops which are treated as shadows. The system can process at 3.22 frames per second for background modeling, and at 2.47 frames per second for result generation. Traffic density and traffic flow are directly influenced by the accuracy of the detection scheme. The accuracy of detection is 68.75% and is heavily dependent on how the system is calibrated.
format text
author Lai, Francis P.
Leong, Alvin Cedric Y.
Ortuoste, Ricardo L.
Yu, Paul K.
author_facet Lai, Francis P.
Leong, Alvin Cedric Y.
Ortuoste, Ricardo L.
Yu, Paul K.
author_sort Lai, Francis P.
title Vision-Based Intelligent System for Traffic Analysis (VISTA)
title_short Vision-Based Intelligent System for Traffic Analysis (VISTA)
title_full Vision-Based Intelligent System for Traffic Analysis (VISTA)
title_fullStr Vision-Based Intelligent System for Traffic Analysis (VISTA)
title_full_unstemmed Vision-Based Intelligent System for Traffic Analysis (VISTA)
title_sort vision-based intelligent system for traffic analysis (vista)
publisher Animo Repository
publishDate 2008
url https://animorepository.dlsu.edu.ph/etd_bachelors/14598
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