Intelligent Traffic Light Control System

0*, Swapna Raghunath **
*-** Department of Electronics and Communication Engineering, G. Narayanamma Institute of Technology and Science, Hyderabad, Telangana, India.
Periodicity:April - June'2021
DOI : https://doi.org/10.26634/jip.8.2.18188

Abstract

The traffic lights are pre-programmed to wait for a certain fixed time after every change in signal. The operation of the traffic lights does not depend on the traffic on the roads and remains constant during its operation. A simple way to ease road traffic is by allocating more time for the vehicles to pass on from heavy traffic roads than roads with relatively less traffic. The proposed system in this paper uses a camera to capture the images of the moving vehicles that connects the traffic signal. The pictures captured are then processed to determine the total number of vehicles present on each road at that instant. The system uses image acquisition, image scaling, image enhancement, followed by object detection in order to estimate the total number of vehicles on roads to regulate the traffic. The dynamic time allocation for traffic on each road is determined based on the actual traffic on the road, and this system will control traffic more effectively than a predetermined time for each road. The system is cost-efficient and does not require any installation of complex machinery to monitor the complete traffic.

Keywords

Image Acquisition, Image Scaling, Image Enhancement, Object Detection.

How to Cite this Article?

Vyshnavi, K., and Raghunath, S. (2021). Intelligent Traffic Light Control System. i-manager's Journal on Image Processing, 8(2), 15-21. https://doi.org/10.26634/jip.8.2.18188

References

[1]. Choudekar, P., Manju, M. K., & Banerjee, S. (2011). Real time traffic control using image processing. Indian Journal of Computer Science and Engineering, 2(1), 6-10.
[2]. Dangi, V., Parab, A., Pawar, K., & Rathod, S. S. (2012). Image processing based intelligent traffic controller. Undergraduate Academic Research Journal (UARJ), 1(1), 13-17. https://doi.org/10.47893/UARJ.2012.1004
[3]. Geethapriya, S., Duraimurugan, N., & Chokkalingam, S. P. (2019). Real-time object detection with Yolo. International Journal of Engineering and Advanced Technology (IJEAT), 8(3S), 578-581.
[4]. Gupta, B., Chaube, A., Negi, A., & Goel, U. (2017). Study on object detection using open CV-Python. International Journal of Computer Applications, 162(8), 17-21.
[5]. Kekre, H. B., Sarode, T., & Thepade, S. (2008). Grid based image scaling technique. International Journal of Computer Science and Applications, 1(2), 95-98.
[6]. Mishra, V. K., Kumar, S., & Shukla, N. (2017). Image acquisition and techniques to perform image acquisition. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 9(01), 21-24. https://doi.org/10.18090/ samriddhi.v9i01.8333
[7]. Niksaz, P. (2012). Automatic traffic estimation using image processing. International Journal of Signal Processing, Image Processing and Pattern Recognition, 5(4), 167-174.
[8]. Thakkar, C., & Patil, R. (2017). Smart traffic control system based on image processing. International Journal for Research in Applied Science & Engineering Technology, 5(7), 573-580.
[9]. Wiley, V., & Lucas, T. (2018). Computer vision and image processing: A paper review. International Journal of Artificial Intelligence Research, 2(1), 29-36. https://doi.org/ 10.29099/ijair.v2i1.42
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.