Optical System to Recognize Car Plate Ownership
DOI:
https://doi.org/10.11113/mjfas.v19n3.2991Keywords:
Detect Oriented FAST and Rotated BRIEF features, matching features, Extract Histograms of Oriented Gradients features, cascade classifierAbstract
The process development of the image processing can solve the problem of detection and recognition of the license plate by taking pictures of the cars and then recognizing them. Most traffic applications rely on automatic vehicle plate detection in parking lots, border control, speed control, etc. In this study, a smart visual system was presented to identify car plates in the College of Science for Girls - University of Baghdad parking lot. The work included distinguishing the car plate and identifying cars, whether they belonged to the college or not. This process was based on the Cascade Classifier method based on the Viola-Jones algorithm, and a database for all car plate features was stored in a file using the proposed method. The recognized car was compared with the characteristics of the database using Oriented FAST and Rotated BRIEF then features were extracted using Histograms of Oriented Gradients. The license plate is recognized when matching features are employed using the matching feature’s function. The results of congruence and discrimination were excellent and very highly efficient. The luminous intensity dependence is considered, as the work is based on the red band of the car's image.
References
J. Shashirangana, H. Padmasiri, D. Meedeniya, and C. Perera. (2020). Automated license plate recognition: a survey on methods and techniques. IEEE Access, 9, 11203-11225. Doi: 10.1109/ACCESS.2020.3047929.
H. K. Abbas and H. J. Mohamad. (2021). Feature extraction in six blocks to detect and recognize english numbers. Iraqi Journal of Science, 62(10), 3790-3803. Doi: 10.24996/ijs.2021.62.10.37.
F. Pan, Y. Yang, L. Zhang, C. Ma, J. Yang, and X. Zhang. (2020). Analysis of the impact of traffic violation monitoring on the vehicle speeds of urban main road: Taking China as an example. Journal of Advanced Transportation, 2020, 6304651. Doi: 10.1155/2020/6304651.
S. Mehtab and J. Sen. (2020).Face Detection Using OpenCV and Haar Cascades Classifiers.
N. Liu, M. Rogers, H. Cui, W. Liu, X. Li, and P. Delmas. (2022). Deep convolutional neural networks for regular texture recognition," (in eng), PeerJ Comput Sci, 8, e869. Doi: 10.7717/peerj-cs.869.
G. K. Ali. (2018). Developing recognition system for new Iraqi license plate. Tikrit Journal of Engineering Sciences, 25(1), 8-11. Doi: http://dx.doi.org/10.25130/tjes.25.1.02.
A. Thakur and A. Kaviti. (2019). Number plate recognition system. 6, 2058-2070.
N. D. Keraf, P. Ehkan, V. H. Kelian, N. M. Noor, H. Darus, and E. Loke. (2020). Automatic vehicle identification system using number plate recognition in POLIMAS. IOP Conference Series: Materials Science and Engineering, 767, 012056. Doi: 10.1088/1757-899x/767/1/012056.
I. Shafi et al. (2021). License plate identification and recognition in a non-standard environment using neural pattern matching. Complex & Intelligent Systems. Doi: 10.1007/s40747-021-00419-5.
A. Siddiqui, M. Alam, H. Kaur, and J. Ahmed. (2021). Implementation of Viola-Jones for detection of facial factors of human for prospect of image recognition.
P. A. Viola and M. J. Jones. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 1, I-I.
P. Akpojotor, A. Adetunmbi, B. Alese, and A. Oluwatope. (2021). Automatic license plate recognition on microprocessors and custom computing platforms: A review. IET Image Processing, 15(12), 2717-2735. https://doi.org/10.1049/ipr2.12262, 2021/10/01 2021.
A. B. Shetty, Bhoomika, Deeksha, J. Rebeiro, and Ramyashree. (2021). Facial recognition using Haar cascade and LBP classifiers. Global Transitions Proceedings, 2(2), 330-335. Doi: https://doi.org/10.1016/j.gltp.2021.08.044.
F. M. Shamrat, A. Majumder, P. Roy Antu, S. Kumar Barmon, I. Nowrin, and R. Ranjan. (2022). Human face recognition applying haar cascade classifier.
T.-H. Tsai and P.-T. Chi. (2022). A single-stage face detection and face recognition deep neural network based on feature pyramid and triplet loss. IET Image Processing, 16(8), 2148-2156. https://doi.org/10.1049/ipr2.12479.
P. Soleimani, D. W. Capson, and K. F. Li. (2021). Real-time FPGA-based implementation of the AKAZE algorithm with nonlinear scale space generation using image partitioning. Journal of Real-Time Image Processing, 18(6), 2123-2134. Doi: 10.1007/s11554-021-01089-9.
H. Nguyen-Quoc, Hoang, V. T. (2021). A revisit histogram of oriented descriptor for facial color image classification based on fusion of color information. Journal of Sensors, 2021, 12. Doi: 10.1155/2021/6296505.
M. F. Aslan, A. Durdu, K. Sabanci, and M. A. Mutluer. (2020). CNN and HOG based comparison study for complete occlusion handling in human tracking. Measurement, 158, 107704. Doi: https://doi.org/10.1016/j.measurement.2020.107704.
http://www.nikon-me.com/ar_ME/product/digital-slr-cameras/d610. (accessed).
Roy, S. (2020). Automatics number plate recognition using convolution neural network.
Soon, C. K., Lin, K. C., Jeng, C. Y. and Suandi, S. A. (2012). Malaysian car number plate detection and recognition system. Australian Journal of Basic and Applied Sciences, 6(3), 49-59.
Putra, C. A., Rahmat B., Hardiristanto S., Purnomo, M. H. (2015). Character recognition of vehicle license plate using extreme learning machine. J. (BISSTECH), Grand Inna Kuta, Bali, Indonesia.
Thakur, A. K. and Kaviti, A. K. (2019). Number plate recognition system.
Keraf, N. D., Ehkan, P., Kelian, V. H., Noor, N. M., Darus, H. and Loke, E. (2020). February. automatic vehicle identification system using number plate recognition in POLIMAS. IOP Conference Series: Materials Science and Engineering, 767(1), 012056. IOP Publishing.
Shafi, I., Hussain, I., Ahmad, J., Kim, P. W., Choi, G. S., Ashraf, I. and Din, S. (2021). License plate identification and recognition in a non-standard environment using neural pattern matching. Complex & Intelligent Systems, 1-13.
Tavallali, P., Yazdi, M. and Khosravi, M. R. (2017). December. An efficient training procedure for viola-jones face detector. 2017 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 828-831). IEEE.
Yaseen, N. O., Al-Ali, S. G. S. and Sengur, A. (2019). November. An efficient model for automatic number plate detection using HOG feature from new north Iraq vehicle images dataset. 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1-6). IEEE.
Habeeb, D., Noman, F., Alkahtani, A. A., Alsariera, Y. A., Alkawsi, G., Fazea, Y. and Al-Jubari, A. M. (2021). Deep-learning-based approach for Iraqi and Malaysian vehicle license plate recognition. Computational Intelligence and Neuroscience, 2021.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 MUHAMMAD SAFWAN ABD AZIZ, Noor M Hashem, Heba Kh Abbas, Haidar J. Mohamad
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
