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...

Full description

Saved in:
Bibliographic Details
Main Authors: Lai, Francis P., Leong, Alvin Cedric Y., Ortuoste, Ricardo L., Yu, Paul K.
Format: text
Language:English
Published: Animo Repository 2008
Subjects:
Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/14598
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
Language: English
Description
Summary: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.