Determine The License Plate of the Vehicle in Violation of a Specific Speed
DOI:
https://doi.org/10.32792/jeps.v12i2.170Keywords:
ACF, Euclidean distance, fps.Abstract
The problem of detecting cars that violate the speed limits set by a certain country or region is a matter
that needs to find an effective way to solve it in order to help the traffic police in this matter, which is
very important, Automatic License Plate Detection (ALPD) is a critical technology for effective traffic
management. It is utilized in a variety of applications including. toll payment systems, parking, and traffic
control, a convolutional neural network (CNN)-based technique for high-accuracy real-time car license
plate detection is presented in this paper. Many modern approaches for detecting car license plates are
used under specified conditions or with significant assumptions. When the car license plate is assessed
photographs as a result of manual capture by traffic cops or camera deviation, there is a degree of
rotation, they perform poorly. Thus, a new framework for multi-directional car plate detection according
to a Region-based Convolutional Network method (R-CNN) that is faster. A number tests have shown
that the suggested method surpasses in terms of accuracy and computing cost, existing state-of-the-art
approaches. The accuracy rate in detecting vehicle license plates reached 98%.
References
A. Rosebrock, “OpenCV Vehicle Detection, Tracking, and Speed Estimation,” Pyimagesearch, pp. 1–74, 2019, [Online]. Available: https://www.pyimagesearch.com/2019/12/02/opencv-vehicle-detection-tracking-and-speed-estimation/.
T. Kumar and D. S. Kushwaha, “An Efficient Approach for Detection and Speed Estimation of Moving Vehicles,” in Procedia Computer Science, 2016, vol. 89, pp. 726–731, doi: 10.1016/j.procs.2016.06.045.
J. Lan, J. Li, G. Hu, B. Ran, and L. Wang, “Vehicle speed measurement based on gray constraint optical flow algorithm,” Optik (Stuttg)., vol. 125, no. 1, pp. 289–295, 2014, doi: 10.1016/j.ijleo.2013.06.036.
E. C. Neto, S. L. Gomes, P. P. Rebouças Filho, and V. H. C. De Albuquerque, “Brazilian vehicle identification using a new embedded plate recognition system,” Meas. J. Int. Meas. Confed., vol. 70, pp. 36–46, 2015, doi: 10.1016/j.measurement.2015.03.039.
S. Javadi, M. Dahl, and M. I. Pettersson, “Vehicle speed measurement model for video-based systems,” Comput. Electr. Eng., vol. 76, pp. 238–248, 2019, doi: 10.1016/j.compeleceng.2019.04.001.
S. L. Jeng, W. H. Chieng, and H. P. Lu, “Estimating speed using a side-looking single-radar vehicle detector,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 607–614, 2014, doi: 10.1109/TITS.2013.2283528.
E. Simley, L. Y. Pao, N. Kelley, B. Jonkman, and R. Frehlich, “LIDAR wind speed measurements of evolving wind fields,” 50th AIAA Aerosp. Sci. Meet. Incl. New Horizons Forum Aerosp. Expo., no. July, 2012, doi: 10.2514/6.2012-656.
M. Mitchell, “the Development of Automobile,” Visible Lang., vol. 44, no. 3, pp. 331–366, 2010, [Online]. Available: http://epublications.bond.edu.au/cgi/viewcontent.cgi?article=1555&context=hss_pubs.
M. R. Karim and A. Dehghani, “Vehicle speed detection in video image sequences using CVS method,” Int. J. Phys. Sci., vol. 5, no. 17, pp. 2555–2563, 2010.
J. Gerat, D. Sopiak, M. Oravec, and J. Pavlovicova, “Vehicle speed detection from camera stream using image processing methods,” Proc. Elmar - Int. Symp. Electron. Mar., vol. 2017-Septe, pp. 201–204, 2017, doi: 10.23919/ELMAR.2017.8124468.
L. Wang and J. Song, “The speed detection algorithm based on video sequences,” Proc. - 2012 Int. Conf. Comput. Sci. Serv. Syst. CSSS 2012, pp. 217–220, 2012, doi: 10.1109/CSSS.2012.62.
A. Nurhadiyatna, B. Hardjono, A. Wibisono, W. Jatmiko, and P. Mursanto, “ITS information source: Vehicle speed measurement using camera as sensor,” 2012 Int. Conf. Adv. Comput. Sci. Inf. Syst. ICACSIS 2012 - Proc., pp. 179–184, 2012.
D. C. Luvizon, B. T. Nassu, and R. Minetto, “Vehicle speed estimation by license plate detection and tracking,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., pp. 6563–6567, 2014, doi: 10.1109/ICASSP.2014.6854869.
“Load vehicle detector using aggregate channel features - MATLAB vehicleDetectorACF
C. N. E. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psoroulas, V. Loumos, and E. Kayafas, “License plate recognition from still images and video sequences: A survey,” IEEE Trans. Intell. Transp. Syst., vol. 9, no. 3, pp. 377–391, 2008, doi: 10.1109/TITS.2008.922938.
Downloads
Published
Issue
Section
License
The Authors understand that, the copyright of the articles shall be assigned to Journal of education for Pure Science (JEPS), University of Thi-Qar as publisher of the journal.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , will be allowed only with a written permission from Journal of education for Pure Science (JEPS), University of Thi-Qar.
Journal of education for Pure Science (JEPS), University of Thi-Qar, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in the Journal of education for Pure Science (JEPS), University of Thi-Qar are sole and exclusive responsibility of their respective authors and advertisers.