Paper Title :Analysis of Traffic Condition by using Machine Vision
Author :Thanida Srichaipetch, Kontorn Chamniprasart
Article Citation :Thanida Srichaipetch ,Kontorn Chamniprasart ,
(2020 ) " Analysis of Traffic Condition by using Machine Vision " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 4-8,
Volume-8,Issue-12
Abstract : Nowadays, there is a large number of students and staffs at Suranaree University of Technology. Since vehicles are
required for daily transportation therefore, the number of students and staffs is having a remarkable impact on the number of
vehicles, traffic congestion, and parking availability within the university area. This research focuses on the analysis of traffic
condition using machine vision in classifying and counting vehicle. Traffic videos were recorded by using an IP camera
installed at the junction area (entrance) of the general inspection building (Building 1). Three types of vehicles were
considered in this research: small, mid-size, and large vehicles. The collected videos were used in machine vision process to
isolate the interested vehicle from the background and to detect its dimensions. The experimental results of vehicle counting
and classification analyzed from 50 traffic videos show that the average accuracy (%) of small vehicles, mid-size vehicles and
large vehicles are 87.51, 90.01 and 93.72 respectively.
Keywords - Detection; BackgroundSubtracktorMOG2; OpenCV
Type : Research paper
Published : Volume-8,Issue-12
Copyright: © Institute of Research and Journals
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Published on 2021-03-15 |
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