OpenCV Based Automatic Detection of Pedestrian Crossing Platform Using Congestion Monitoring - A Survey

Authors

  • Kantharaju. V  Assistant Professor& HOD of ISE Department, KNS Institute of Technology, Bangalore, Karnataka, India
  • Chandu Priya V  Student, KNS Institute of Technology, Bangalore, Karnataka, India
  • Kousalya S  Student, KNS Institute of Technology, Bangalore, Karnataka, India
  • Parvati  Student, KNS Institute of Technology, Bangalore, Karnataka, India
  • Nagaveni B C  Student, KNS Institute of Technology, Bangalore, Karnataka, India

DOI:

https://doi.org//10.32628/IJSRSET22937

Keywords:

Infrared, Traffic Congestion Monitoring, Automatic Uplifting

Abstract

Road traffic congestion and pedestrian accidents are one of the major issues being faced worldwide. The reasons behind these accidents are mainly due to the risk when crossing or walking on road in urban and rural areas under heavy traffic. In order to avoid such circumstances a new idea is proposed- “Automatic uplifting of pedestrian crossing platform using traffic congestion monitoring”. The pedestrian and traffic congestion is constantly monitored using an IR (Infrared) sensor module. At the time when pedestrian density is more, the traffic signal turns red for the vehicles and let the pedestrian walk through uplifted crossing platform. The proposed system consists of motorized platform placed on the zebra crossing line which automatically uplifts when the infrared sensor senses and count the crowded pedestrians at the signal point. This method ensures safety in pedestrian crossing and will also not let drivers to break the rules, which may lead to accidents

References

  1. N. Diaz, J. Guerra, and J. Nicola. “Smart Traffc Light Control System”. In: 2018 IEEE 3rd Ecuador Tech. Chapters Meet, ETCM 2018, 2018.
  2. I. Santos-González, P. Caballero-Gil, A. Rivero-García, and C. Caballero-Gil. “Priority and collision avoidance system for traffc lights”. Ad Hoc Networks, vol. 94, pp. 101931, 2019.
  3. A. H. Akoum. “Automatic traffc using image processing.” Journal of Software Engineering and Applications, vol. 10, no. 9, pp. 765-776, 2017.
  4. H. Joo, S. H. Ahmed and Y. Lim. “Traffc signal control for smart cities using reinforcement learning”. Computer Communications, vol. 154, pp. 324-330, 2020.
  5. A. Dumka and A. Sah. Smart Ambulance Traffc Management System (SATMS)-a Support for Wearable and Implantable Medical Devices. Elsevier Inc., Amsterdam, 2020.
  6. M. M. Elkhatib and A. S. Alsamna. “Smart Traffc Lights using Image Processing Algorithms”. In: 2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering, pp. 1-6, 2019.
  7. E. L. H. Imad. “Proposed Solutions for Smart Traffc Lights using Machine Learninig and Internet of Thing”. In: 2019 International Conference on Wireless Networks and Mobile Communications, pp. 1-6, 2019.
  8. L. F. P. Oliveira, L. T. Manera and P. D. G. Luz. “Smart Traffc Light Controller System”. In: 2019 Sixth International Conference on Internet of Things: Systems, Management and Security, pp. 155- 160, 2019.
  9. T. A. Kareem and M. K. Jabbar. Design and Implementation Smart Traffic Light Using GSM and IR. pp. 1-5, 2018.
  10. M. B. Natafgi, M. Osman, A. S. Haidar and L. Hamandi. Smart Traffic Light System Using Machine Learning”. In: 2018 IEEE International Multidisciplinary Conference on Engineering Technology, pp. 1-6, 2019.
  11. M. Z. Ismail, A. Z. A. Lutf and M. A. M. Roslan. “Smart traffc light for emergency vehicle by using arduiono”. AIP Conference Proceedings, vol. 2129, pp. 1-6, 2019.
  12. Andreas, C. R. Aldawira, H. W. Putra, N. Hanafah, S. Surjarwo and A. Wibisurya. “Door security system for home monitoring based on ESp32”. Procedia Computer Science, vol. 157, pp. 673-682, 2019.
  13. J. Redmon and A. Farhadi. YOLOv3: An Incremental Improvement, 2018.
  14. Y. Jamtsho, P. Riyamongkol and R. Waranusast. “Real-time Bhutanese license plate localization using YOLO”. ICT Express, vol. 6, no. 2, pp. 121-124, 2019.
  15. Y. Jamtsho, P. Riyamongkol and R. Waranusast. Real-time License Plate Detection for Non-helmeted Motorcyclist using YOLO. ICT Express, Amsterdam, 2020.
  16. F. Koyanagi. ESP32 With ESP-Now Protocol, 2019.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2022-06-30

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Kantharaju. V, Chandu Priya V, Kousalya S, Parvati, Nagaveni B C, " OpenCV Based Automatic Detection of Pedestrian Crossing Platform Using Congestion Monitoring - A Survey , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 3, pp.52-57, May-June-2022. Available at doi : https://doi.org/10.32628/IJSRSET22937