Volume 15, Issue 4 (3-2019)                   JSDP 2019, 15(4): 41-56 | Back to browse issues page

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Abdollahi M, Khosravi H. Design and Implementation of Real-Time License Plate Recognition System in Video Sequences. JSDP. 2019; 15 (4) :41-56
URL: http://jsdp.rcisp.ac.ir/article-1-665-en.html
Shahrood University of Technology
Abstract:   (1574 Views)

An automatic Number Plate Recognition (ANPR) is a popular topic in the field of image processing and is considered from different aspects, since early 90s. There are many challenges in this field, including; fast moving vehicles, different viewing angles and different distances from camera, complex and unpredictable backgrounds, poor quality images, existence of multiple plates in the scene, variable lighting conditions throughout the day, and so on. ANPR systems have many applications in today’s traffic monitoring and toll-gate systems.
In this paper, a real-time algorithm is designed and implemented for simultaneous detection and recognition of multiple number plates in video sequences. Already some papers on plate localization and recognition in still? images have been existed , however, they do not consider real time processing. While for the related applications, real-time detection and recognition of multiple plates on the scene is very important. Unlike methods with high computational complexity, we apply simple and effective techniques for being real-time. At first, background is modeled using Gaussian Mixture Model (GMM) and moving objects are determined. Then, plate candidate regions are found by vertical edge detection and horizontal projection. After that, license plates are localized and extracted by morphological operations and connected components analysis. When plates were are detected, their characters are separated with another algorithm. Finally a neural network is applied for character recognition.
This system is implemented in C++ using OpenCV library. The average localization time per frame is 25 ms and total processing time, including localization and recognition, is 40 ms that can be used in real-time applications. The proposed method is evaluated on videos from highway cameras and the detection rate of 98.79% and recognition rate of 97.83% is obtained. Our real-time system can also recognize multiple plates of different types in each frame. Experimental results show that our method have higher speed and better recognition rate than previous works therefore it is suitable for real-time applications.

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Type of Study: Research | Subject: Paper
Received: 2017/10/29 | Accepted: 2019/01/9 | Published: 2019/03/8 | ePublished: 2019/03/8

1. [1] B.Y. Amirgaliyev, C.A. Kenshimov, K.K. Kuatov, M.Z. Kairanbay, Z.Y. Baibatyr and A.K. Jantassov, "License plate verification method for automatic license plate recognition systems," Twelve International Conference on Electronics Computer and Computation (ICECCO), pp. 1-3, 2015. [DOI:10.1109/ICECCO.2015.7416892]
2. [2] Y. Li and H. Wu, "Design and Implementation of the License Plate Positioning System Based on the DSP," International Conference on Computer Sciences and Applications (CSA), pp. 635-638, 2013. [DOI:10.1109/CSA.2013.153]
3. [3] Y.K. Wang, C.T. Fan and J.F. Chen, "Traffic Camera Anomaly Detection," 22nd International Conference on Pattern Recognition (ICPR), pp. 4642-4647, 2014. [DOI:10.1109/ICPR.2014.794] [PMCID]
4. [4] G. A. Montazer and M. Shayestehfar, "Iranian license plate identification with fuzzy support vector machine," Journal of Signal and Data Processing (JSDP), vol 12, no. 1, pp. 47-56, 2015.
5. [5] S. Du, M. Ibrahim, M. Shehata and W. Badawy, "Automatic license plate recognition (ALPR): A state-of-the-art review," IEEE Transactions on circuits System video Technology, vol. 23, no. 2, pp. 311-325, 2013. [DOI:10.1109/TCSVT.2012.2203741]
6. [6] M.M.I. Chacon and S.A. Zimmerman, "License plate location dynamic PCNN scheme," International Joint Conference Neural Network, 2003.
7. [7] B. Chenaghlou and M. Rahmati, "Online license plate detection in complex background images using fuzzy math morphology," in 5th Conference of the Machine Vision and Image Processing, Tabriz, Iran, 2008.
8. [8] V. Abolghasemi and A. Ahmadifard, "An edge-based color-aided method for license plate detection," Image and Vision Computing, vol. 27, pp. 1134-1142, 2009. [DOI:10.1016/j.imavis.2008.10.012]
9. [9] T. Duan, T. Hong Du, T. Phuoc and N. Hoang, "Building an automatic vehicle license plate recognition system," in International Conference Computer Science, Can Tho, Vietnam, 2005.
10. [10] Y. Wang, W. Lin and S. Horng, "A sliding window technique for efficient license plate localization based on discrete wavelet transform," Expert Systems with Applications, vol. 38, pp. 3142-3146, 2011. [DOI:10.1016/j.eswa.2010.08.106]
11. [11] J. Li and M. Xie, "A color and texture feature based approach to license plate location," in international conference on computational intelligence and security, 2007. [DOI:10.1109/CIS.2007.71]
12. [12] S. Rastegar, R. Ghaderi, G. R. Ardeshir and Nima Asadi, "An intelligent control system using an efficient License Plate Location and Recognition Approach," International Journal of Image Processing, vol. 3, no. 5, pp. 252-264, 2009.
13. [13] L. Yu, "Research on Edge Detection in License Plate Recognition," in Proceedings of International Conference on Computer Application and System Modeling, 2012.
14. [14] A. George and V.J. Pillai, "VNPR system using artificial neural network," International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-6, 2016. [DOI:10.1109/ICCPCT.2016.7530282]
15. [15] L. Fuliang and G. Shuangxi, "Character Recognition System Based on Back Propagation Neural Network," in International Conference on Machine Vision and Human-Machine Interface (MVHI), 2010.
16. [16] A. Nagare, "License plate character recognition system using neural network," International Journal of Computer Applications, vol. 25, no. 10, July 2011. [DOI:10.5120/3147-4345]
17. [17] M. Nejati and H. Poorghasem, " Identification of license plate characters using the mixing structure of experts," Journal of electronics Industries, vol. 3, no. 2, pp. 41-60, 2012.
18. [18] D.K. Yadav, "Efficient method for moving object detection in cluttered background using Gaussian Mixture Model," IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 943-948, 2014. [DOI:10.1109/ICACCI.2014.6968502]
19. [19] C. Stauffer, W. Eric and L. Grimson, "Learning patterns of activity using real-time tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 747-757, 2000. [DOI:10.1109/34.868677]
20. [20] C. Stauffer and W.E.L. Grimson, "Adaptive background mixture models for real-time tracking," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA, 23-25 June 1999.
21. [21] H. Scharr, "Optimal Operators in Digital Image Processing," Dissertation (in German), 2000.
22. [22] L.L. Chun and Y.S. Chung, "Modified unsharp masking detection using Otsu thresholding and Gray code," IEEE International Conference on Industrial Technology (ICIT), pp. 787-791, 2016.
23. [23] M. Sarfraz, M. Ahmed and S. Ghazi, "Saudi Arabian license plate recognition system," in Proceedings of International Conference on Geom. Model. Graph., 2003.
24. [24] V. Abolghasemi and A. Ahmadifard, "Application of IFT transformation in license plate recognition system," in Third Conference on Information and Knowledge Technology, Mashhad, Iran, 2007.
25. [25] H. Zhang, W. Jia, X. He and Q. Wu, "Learning-based license plate detection using global and local features," Pattern Recognition, pp. 1102-1105, 2006.
26. [26] T. Duan, D. Duc and T. Du, "Combining Hough transform and contour algorithm for detecting vehicles' license-plates," in Proceedings of International Symposium on Intelligent Multimedia Video Speech Processing, 2004.
27. [27] K. Deb and K. Jo, "A vehicle license plate detection method for intelligent transportation system applications," Cybernetic System International Journal, vol. 40, no. 8, pp. 689-705, 2009. [DOI:10.1080/01969720903294601]
28. [28] C. Anagnostopoulos, T. Alexandropoulos, V. Loumos and E. Kayafas, "Intelligent traffic management through MPEG-7 vehicle flow surveillance," in Proceedings of IEEE International Symposium Modern Computing, 2006. [DOI:10.1109/JVA.2006.30]
29. [29] S. Wang and H. Lee, "A cascade framework for a real-time statistical plate recognition system," IEEE Transactions on Information Forensics Security, vol. 2, no. 2, pp. 267-282, June 2007. [DOI:10.1109/TIFS.2007.897251]
30. [30] X. Shi, W. Zhao and Y. Shen, "Automatic license plate recognition system based on color image processing," Lecture Notes computer science, vol. 3483, pp. 1159-1168, 2005. [DOI:10.1007/11424925_121]
31. [31] H. Lee, S. Chen and S. Wang, "Extraction and recognition of license plates of motorcycles and vehicles on highways," in Proceedings of International Conference on Pattern Recognition, 2004.
32. [32] M. S. Sarfraz, A. Shahzad, M. A. Elahi, M. Fraz, I. Zafar and E. A. Edirisinghe, "Real-time automatic license plate recognition for CCTV forensic applications," Journal of Real-Time Image Processing, vol. 8, pp. 285-295, 2013. [DOI:10.1007/s11554-011-0232-7]
33. [33] M. Wang, Y. Liu, B. Liao, Y. Lin and M. Horng, "A vehicle license plate recognition system based on spatial/frequency domain filtering and neural networks," in Proceedings of Computing Collective Intelligence Technology Application, LNCS 6423, 2010. [DOI:10.1007/978-3-642-16696-9_8]
34. [34] S. Chang, L. Chen, Y. Chung and S. Chen, "Automatic License Plate Recognition," IEEE Transaction on Intelligent Transportation Systems, vol. 5, no. 1, pp. 42-53, 2004. [DOI:10.1109/TITS.2004.825086]
35. [35] P. Comelli, P. Ferragina, M. Granieri and F. Stabile, "Optical recognition of motor vehicle license plates," IEEE Transaction on Vehicles Technology, vol. 44, no. 4, pp. 790-799, November 1995. [DOI:10.1109/25.467963]
36. [36] T. Naito, T. Tsukada, K. Yamada, K. Kozuka and S. Yamamoto, "Robust license-plate recognition method for passing vehicles under outside environment," IEEE Transaction on Vehicles Technology, vol. 49, no. 6, pp. 2309-2319, November 2000. [DOI:10.1109/25.901900]
37. [37] H. Khosravi, "A Sliding and Classifying Approach Towards Real Time Persian License Plate Recognition," International Journal of Engineering (IJE), vol. 28, no. 1, pp. 74-80, January 2015. [DOI:10.5829/idosi.ije.2015.28.01a.10]

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