© The Institution of Engineering and Technology
In this study, the authors propose a real-time multiple licence plate (LP) detection algorithm for dense traffic conditions which is of vital importance in this modern era due to increased traffic congestion. The chromatic component of YDbDr colour space is proposed to detect the blue regions, whereas a simple yet effective colour detection method is used to identify yellow LP regions. The low-intensity pixel values are eliminated as a pre-processing step to enhance the LP regions and Otsu method is used to obtain the binary image. The candidate regions are acquired by using the connected component analysis. The false candidate regions are by large rejected by inspecting the area and aspect ratio of LPs. Additionally, a two-layered false LP detection approach has been introduced to remove fake LP regions. Experimental results in practical scenarios carried out in various weather conditions show that the proposed method is highly effective in coping with various illumination conditions to accurately detect the multiple vehicle LPs with an accuracy of 93.86%. The average processing time per image is 0.33 s that can achieve real-time performance.
References
-
-
1)
-
3. Lee, E.R., Kim, P.K., Kim, H.J.: ‘Automatic recognition of a car license plate using color image processing’. Proc. IEEE Int. Conf. Image Process, November 1994, vol. 2, pp. 301–305.
-
2)
-
20. Wang, F., Man, L., Wang, B., et al: ‘Fuzzy-based algorithm for color recognition of license plates’, Pattern Recognit. Lett., 2008, 29, (7), pp. 1007–1020 (doi: 10.1016/j.patrec.2008.01.026).
-
3)
-
1. Davies, P., Emmott, N., Ayland, N.: ‘License plate recognition technology for toll violation enforcement’, Inst. Electr. Eng. Colloq. Image Anal. Transp. Appl., 1990, 7, pp. 1–5.
-
4)
-
2. Yamaguchi, K., Nagaya, Y., Ueda, K., et al: ‘A method for identifying specific vehicles using template matching’. Proc. IEEE Int. Conf. Intelligent Transportation Systems, 1999, pp. 8–13.
-
5)
-
16. Bellas, N., Chai, SM., , Dwyer, M., et al: ‘FPGA implementation of a license plate recognition SoC using automatically generated streaming accelerators’. Proc. IEEE Int. Parallel Distributed Processing Symp., April 2006, pp. 8–15.
-
6)
-
8. Zheng, D., Zhao, Y., Wang, J.: ‘An efficient method of license plate location’, Pattern Recognit. Lett., 2005, 26, (15), pp. 2431–2438 (doi: 10.1016/j.patrec.2005.04.014).
-
7)
-
18. Unaisa, V.A., Shena, K.K.: ‘Identify number plate based on pixel variation analysis’. Int. Conf. Security and Authentication (SAPIENCE), 2014, pp. 118–124.
-
8)
-
10. Faradji, F., Rezaie, A.H., Ziaratban, M.: ‘A morphological-based license plate location’. Proc. IEEE Int. Conf. Image Processing, September–October 2007, pp. 57–60.
-
9)
-
26. Jinxue, S., Li, Y., Yongqiang, W., et al: ‘An object color recognition algorithm based on study-expansion method’. Proc. Ninth Int. Conf. on Electronic Measurement & Instruments (ICEMI ’09), 16–19 August 2009, pp. 202–205.
-
10)
-
31. Gonzalez, R.C., Woods, R.E.: ‘Adjacency, connectivity, regions, and boundaries: ‘Digital image processing’’ (Prentice-Hall, Upper Saddle River, New Jersey, USA, 2002, 2nd edn.), pp. 66–68.
-
11)
-
30. Otsu, N.: ‘A threshold selection method from gray-level histograms’, IEEE Trans. Syst. Man Cybern., 1979, 9, (3), pp. 62–66.
-
12)
-
21. Deb, K., Jo, K.-H.: ‘A vehicle license plate detection method for intelligent transportation system applications’, Cybern. Syst. Int. J., 2009, 40, (8), pp. 689–705 (doi: 10.1080/01969720903294601).
-
13)
-
15. Wu, B.F., Lin, S.P., Chiu, C.C.: ‘Extracting characters from real vehicle license plates out-of-doors’, IET Comput. Vis., 2007, 1, (1), pp. 2–10 (doi: 10.1049/iet-cvi:20050132).
-
14)
-
29. Runmin, W., Nong, S., Rui, H., et al: ‘License plate detection using gradient information and cascade detectors’, Opt. – Int. J. Light Electron Opt., 2014, 125, (1), pp. 186–190 (doi: 10.1016/j.ijleo.2013.06.008).
-
15)
-
22. Deb, K., Jo, K.-H.: ‘Segmenting the license plate region using a color model’, in Yin, P.-Y. (Ed.): ‘Pattern recognition’ (InTech: Rijeka, Croatia), 2009, pp. 401–418, .
-
16)
-
28. Li, J.: ‘Multi-features-based license plate detection in nighttime environment’, J. Softw., 2014, 9, (9), pp. 2353–2360.
-
17)
-
19. Shi, X., Zhao, W., Shen, Y.: ‘Automatic license plate recognition system based on color image processing’, Lect. Notes Comput. Sci., 2005, 3483, pp. 1159–1168 (doi: 10.1007/11424925_121).
-
18)
-
17. Wu, P., Chen, H.H., Wu, R.J., et al: ‘License plate extraction in low resolution video’, Proc. Int. Conf. Pattern Recognit., 2006, 1, pp. 824–827.
-
19)
-
27. Khekade, A., Bhoyar, K.: ‘Shadow detection based on RGB and YIQ color models in color aerial images’. Proc. Int. Conf. on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), 25–27 February 2015, pp. 144–147.
-
20)
-
7. Bai, H., Liu, C.: ‘A hybrid license plate extraction method based on edge statistics and morphology’, Proc. Int. Conf. Pattern Recognit., 2004, 2, pp. 831–834.
-
21)
-
14. Matas, J., Zimmermann, K.: ‘Unconstrained license plate and text localization and recognition’. Proc. IEEE Int. Conf. Intelligent Transport Systems, September 2005, pp. 225–230.
-
22)
-
13. Qin, Z., Shi, S., Xu, J., et al: ‘Method of license plate location based on corner feature’, Proc. World Congr. Intell. Control Autom., 2006, 2, pp. 8645–8649.
-
23)
-
6. Jingyu, D., Sanyuan, Z., Xiuzi, Y., et al: ‘Chinese license plate localization in multi-lane with complex background based on concomitant colors’, IEEE Intell. Transp. Syst. Mag.2015, 7, (3), pp. 51–61 (doi: 10.1109/MITS.2015.2412146).
-
24)
-
9. Wang, S., Lee, H.: ‘Detection and recognition of license plate characters with different appearances’, Proc. Int. Conf. Intell. Transp. Syst., 2003, 2, pp. 979–984.
-
25)
-
25. Fooladivanda, A., Chehrerazi, N., Sadri, S., et al: ‘Automatic segmentation of pallet images using the 2-D wavelet transform and YUV color space’. Proc. 18th Iranian Conf. on Electrical Engineering (ICEE), 11–13 May 2010, pp. 209–214.
-
26)
-
23. Oksuz, C., Gullu, M.K.: ‘Adaptive local thresholding based number plate detection’. Proc. 23th Signal Processing and Communications Applications Conf. (SIU), 16–19 May 2015, pp. 1437–1440.
-
27)
-
5. Tian, B., Li, Y., Li, B., et al: ‘Rear-view vehicle detection and tracking by combining multiple parts for complex urban surveillance’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (2), pp. 597–606 (doi: 10.1109/TITS.2013.2283302).
-
28)
-
24. Kebin, A., Sun, J., Du, W.: ‘A brightness adjustment method for MPEG-2 com pressed video editing’. 2006 Digest of Technical Papers; IEEE Int. Conf. on Consumer Electronics, 7–11 January 2006, pp. 311–312.
-
29)
-
12. Anagnostopoulos, C.-N.E., Anagnostopoulos, I.E., Psoroulas, I.D., et al: ‘License plate recognition from still images and video sequences: a survey’, IEEE Trans. Intell. Transp. Syst., 2008, 9, (3), pp. 377–391 (doi: 10.1109/TITS.2008.922938).
-
30)
-
31)
-
11. Lee, H.J., Chen, S.Y., Wang, S.Z.: ‘Extraction and recognition of license plates of motorcycles and vehicles on highways’. Proc. Int. Conf. Pattern Recognition, 2004, pp. 356–359.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2016.0008
Related content
content/journals/10.1049/iet-its.2016.0008
pub_keyword,iet_inspecKeyword,pub_concept
6
6