Monitoring and Counting the Vehicles in Vietnam’s Traffic

Authors

  • Phuoc Tho Tuong Ho Chi Minh City University of Technology and Education, Vietnam
  • Truong Thinh Nguyen Ho Chi Minh City University of Technology and Education, Vietnam
  • Ngoc Phuong Nguyen Ho Chi Minh City University of Technology and Education, Vietnam

Corressponding author's email:

thotp@hcmute.edu.vn

Keywords:

monitoring, counting, vehicles, traffic, image

Abstract

The current situation of traffic in Vietnam is very complicated, the infrastructure not keeping up with the growth of the city, resulting in frequent traffic congestion and causing great  economic damage. Several solutions have been implemented to reduce traffic congestion, as well as to control the observance of traffic rules but very expensive and not at all effective. In this context, a system to monitor and control the means of communication proves to be necessary. This paper introduces the basic idea of a software that can identify and count the vehicles involved in traffic monitoring systems and traffic control. System image-capture sensors include cameras placed on roads. With the data collected from these cameras, the software will perform the algorithms for identification and analysis of the means of comunication in traffic. From the calculated data, the software can help handling these situations, with the involvement of the robot to control and monitor the traffic.

Downloads: 0

Download data is not yet available.

References

Katsuhiko Otaga, Discrete-Time Control Systems. Pearson Education. Prentice Hall, Upper Saddle River, New Jersey 07458.

Setsuo Hashimoto, Naoyuki Kubota, and Fumio Kojima, Human Hand Detection Using Evolutionary Computation for Gestures Recognition of a Partner Robot. Faculty of Economics, Kyoto Gakuen University, 1-1 Ootani, Nanjyo, Sogabe-cho, Kameoka, Kyoto, 621-8551, Japan.

Published

01-11-2021

How to Cite

[1]
P. T. Tuong, T. T. Nguyen, and N. P. Nguyen, “Monitoring and Counting the Vehicles in Vietnam’s Traffic”, JTE, vol. 6, no. 4, pp. 72–79, Nov. 2021.

Issue

Section

Research Article

Categories

Most read articles by the same author(s)