Automatic Traffic Management System for Emergency Vehicles

Jamuna*, Vinay M. G.**
*-** Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
Periodicity:January - March'2019
DOI : https://doi.org/10.26634/jdp.7.1.16445

Abstract

One of the major problems faced by people in their daily life is traffic. An efficient management system is required to provide efficient traffic control system. Identification of particular vehicle in the traffic flow is a notable task in the traffic management system. In this research work, the authors propose an automatic traffic management system for vehicle detection and counting and automatic signals Scheduling. The camera supplies video input to the processing engine. Initially, the video will be streaming on all four roads of the traffic circle. The values will be read frame by frame in the streaming video of these roads. Camera sends all the captured input images to the processing engine and this works based on the neural network. The traffic flow shows the traffic state in fixed time interval and helps to manage and control the traffic, especially when there is a heavy traffic and will consider emergency vehicles like ambulance and fire brigades, giving them priority to go.

Keywords

Object Detection, YOLO, Signal Scheduling, Deep Learning, Computer Vision.

How to Cite this Article?

Jamuna & Vinay, M. G., (2019). Automatic Traffic Management System for Emergency Vehicles. i-manager’s Journal on Digital Signal Processing, 7(1), 39-43. https://doi.org/10.26634/jdp.7.1.16445

References

[1]. Blaschko, M. B., & Lampert, C. H. (2008, October). Learning to localize objects with structured output regression. In European Conference on Computer Vision (pp. 2-15). Springer, Berlin, Heidelberg. https://doi.org/ 10.1007/978-3-540-88682-2_2
[2]. Buhler, K., Lambert, J., & Vilim, M. (n.d). Yoloflow realtime object tracking in video CS 229 course project. Retrieved from https://pdfs.semanticscholar.org/989c/ 7cdafa9b90ab2ea0a9d8fa60634cc698f174.pdf
[3]. Hegde, R., Sali, R. R., & Indira, M. S. (2013). RFID and GPS based automatic lane clearance system for ambulance. International Journal of Advanced Electrical and Electronics Engineering, 2(3), 102-107.
[4]. Jadhav, P., Kelkar, P., Patil, K., & Thorat, S. (2016). Smart traffic control system using image processing. International Research Journal of Engineering and Technology, 3(3), 1207-1211.
[5]. Jensen, M. B., Nasrollahi, K., & Moeslund, T. B. (2017). Evaluating state-of-the-art object detector on challenging traffic light data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 9-15).
[6]. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779-788).
[7]. Tao, J., Wang, H., Zhang, X., Li, X., & Yang, H. (2017, October). An object detection system based on YOLO in th traffic scene. In 2017 6 International Conference on Computer Science and Network Technology (ICCSNT) (pp. 315-319). IEEE. https://doi.org/10.1109/ICCSNT.2017. 8343709
[8]. Yang, W., Zhang, J., Wang, H., & Zhang, Z. (2018, May). A vehicle real-time detection algorithm based on YOLOv2 framework. In Real-Time Image and Video Processing 2018 (Vol. 10670, p. 106700N). International Society for Optics and Photonics. https://doi.org/10.1117/ 12.2309844
[9]. Zhang, J., Huang, M., Jin, X., & Li, X. (2017). A realtime chinese traffic sign detection algorithm based on modified YOLOv2. Algorithms, 10(4), 127. https://doi.org/ 10.3390/a10040127
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
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

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.