Traffic Surveillance Using Smart Drone

Authors

  • Prof. Mounica B  Information Science, New Horizon College of Engineering, Bangalore, Karnataka, India
  • Sathya N  Information Science, New Horizon College of Engineering, Bangalore, Karnataka, India
  • Likitha R  Information Science, New Horizon College of Engineering, Bangalore, Karnataka, India
  • Meghana C A  

DOI:

https://doi.org//10.32628/CSEIT2062110

Keywords:

Drone, Camera, Image, Vehicle, Server

Abstract

Day by day the number of vehicles is increasing very fast as the demand is increasing. So, the details of the vehicles are very important to maintain for the government of a country. Information like ownership, insurance, emission, road tax etc., need to be maintained and accessed very efficiently and easily. Even for crime purpose the vehicles are used. So, depending on the demand of the requirements we have proposed this model for real time vehicle monitoring and intimating for violation of traffic rules using drone. In our proposed system, the drone is fitted with cameras and Raspberry Pi. The drone will keep on monitoring the non-parking areas from above the level of ground. The drone will capture the image and detect the vehicle and if the vehicle is not moving after two minutes, transmit the vehicle image to the server along with the road signal code.

References

  1. Anuj Puri, “A Survey of Unmanned Aerial Vehicles (UAV) for Traffic Surveillance”, IEEE 2015.
  2. Mouna Elloumi, Riadh Dhaou, Benoit Escrig, Hanen Idoudi, Leila Azouz Saidane, “Monitoring Road Traffic with a UAV-based System,” April 2018.
  3. Liu, Y., Yao, L., Shi, Q., Ding, J. (2014). Optical flow based urban road vehicle tracking. In 2013 Ninth International Conference on Computational Intelligence and Security. https://doi.org/10.1109/cis.2013.89. IEEE.
  4. Sergio Patricio Figueroa Sanz, James Tran, Chung Yu Wang, “Street Parking Detection,” in IEEE, 2016
  5. Ferryman, J.M., Worrall, A.D., Sullivan, G.D., Baker, K.D. (1995). A generic deformable model for vehicle recognition. In Proceedings of the British Machine Vision Conference 1995. https://doi.org/10.5244/c.9.13. British Machine Vision Association.
  6. Arsenii Surmin, Anastasiia Rozhok, Lorenzo Damiani, Pietro Giribone and Roberto Revetria 2014
  7. Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X. (2018). Object detection with deep learning: A review. arXiv e-prints, arXiv:1807.05511.
  8. Z. Zhang, X. Li, H. Yuan, and F. Yu, “A street parking system using wireless sensor networks,” International Journal of Distributed Sensor Networks, vol. 2013, 2013.
  9. Luo, Z. (2018). Traffic analysis of low and ultra-low frame-rate videos, Doctoral dissertation. Université de Sherbrooke.
  10. Geiger, A. (2012). Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2012.6248074. IEEE, (pp. 3354–3361).
  11. Hirahara, K.; Ikeuchi, K., "Detection of street-parking vehicles using line scan camera and scanning laser range sensor," Intelligent Vehicles Symposium, 2003. Proceedings. IEEE, vol., no., pp.656,661, 9-11 June 2003

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2020-04-30

Issue

Volume 6, Issue 2, March-April-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Prof. Mounica B, Sathya N, Likitha R, Meghana C A, " Traffic Surveillance Using Smart Drone , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 2, pp.356-363, March-April-2020. Available at doi : https://doi.org/10.32628/CSEIT2062110