An adaptive integrated rule-based algorithm for license plate localization

• Autorzy: Paunwala C. N. , Patnaik S.
• Języki publikacji: EN

Abstrakty

EN

This paper addresses a license plate localization (LPL) algorithm for a complex background. Most of LPL algorithm works on restricted conditions, as well as on a principle of sequential elimination of blocks from image level to final LP candidate region. In most of algorithms, blocks are filtered out for not satisfying required LP features in a top-down approach and this may result in a poor efficiency in a complex scenario. The major steps of the proposed approach are adaptive edge mapping, saliency measure of edge based rules with confidence level estimation using fuzzy rules and final step for reassessment of decision by colour attributes filtering. The proposed algorithm is adaptive to across the country variations in LP standards, as well as it is tested on two data sets each one consisting of more than 700 images, set-1 being for good images while set-2 including only constrained images. The algorithm is tested for a low contrast due to overexposure or poor lighting, existence of multiple plates, variation in aspect ratio and compatible background conditions. It has been observed, that the performance degradation imposing complex condition is nominal.

Słowa kluczowe

EN

license plate localization; saliency rules evaluation; statistical inconsistency; cognitive approach

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2012
• Tom: Vol. 20, No. 4
• Strony: 323--334
• Opis fizyczny: Bibliogr. 41 poz., il., rys., wykr.

Twórcy

autor

Paunwala C. N.

autor

Patnaik S.

• Deptartment of Electronics and Communication Engineering, Sarvajanik College of Engineering Technology, Surat, 395001 India,

Bibliografia

• [1] Bob Williams, Intelligent Transport Systems Standards, Artech House Publications, Norwood, MA, 2008.
• [2] S. Katoh and H. Yanagawa: Research and development on on-board server for Internet ITS: Proc. IEEE Symposium on Applications and the Internet Workshops, pp. 35-36, Nara, 2002.
• [3] S. Draghici: A neural network based artificial vision system for license plate recognition. Int. J. Neural Syst. 8, 113-126 (1997).
• [4] D. S. Gao and J. Zhou: Car license plates detection from complex scene. Proc. Int. Conf. Signal Process. 2, pp. 1409-1414, Beijing, 2000.
• [5] D. Zheng, Y. Zhao, and J. Wang: An efficient method of license plate location. Elsevier Pattern Recogn. Lett. 26, 2431-2438 (2005).
• [6] C. N. Paunwala and S. Patnaik: A novel multiple license plate extraction technique for complex background in Indian traffic conditions. Int. J. Image Processing, Computer Science Journals 4, 106-118 (2010).
• [7] B. Hongliang and L. Changping: A hybrid license plate extraction method based on edge statistics and morphology. Int. Conf. Pattern Recogn. 2, pp. 831-834, Cambridge, 2004.
• [8] C. Wu, L. C. On, C. H. Weng, T. S. Kuan, and K. Ng: A Macao license plate recognition system. in 4th Int. Conf. Mach. Learn. Cybernetics, pp. 4506-4510, Guangzhou, 2005.
• [9] T. D. Duan, T. L. H. Du, T. V. Phuoc, and N. V. Hoang: Building an automatic vehicle license-plate recognition system. Proc. Int. Conf. Comput. Sci., pp. 59-63, Can Tho, 2005.
• [10] C. T. Hsieh, Y.-S.Jua, and K.-M. Hung: Multiple licens plate detection for complex background. Int. Conf. Advanced Information Networking and Applications, pp. 389-392, Taipei, 2005.
• [11] C. N. Paunwala, and S. Patnaik: An improved license plate extraction technique based on top-hat transformation and prolonged Haar Wavelet analysis. ACM Int. Conf. And Workshop on Emerging Trends in Technology, pp. 618-622, New York, 2010.
• [12] N. Otsu: A threshold selection method from gray-level histograms. IEEE T. Sys. Man Cyb. 9, 62-66, (1979).
• [13] K. I. Kim, K. Jung, and J. H. Kim, Colour Texture-Based Object Detection: An Application to License Plate Localization, Springer-Verlag, Berlin, 2000.
• [14] X. Zhang, P. Shen, J. Bai, J. Lei, Y. Hu, Y. Xiao, B. Li, and D. Qi: License plate detection using AdaBoost algorithm. IEEE Int. Conf. on Information and Automation, pp.1705-1710, Harbin, 2010.
• [15] J. Xiong, S. Du, D. Gao, and Q. Shen: Locating car license plate under various illumination conditions using genetic algorithm. Proc. 7th Int. Conf. on Signal Process., pp. 2502-2505, Beijing, 2004.
• [16] 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 T. Intel. Transp. 9, 377-391 (2008).
• [17] G. Li, R. Zeng, and L. Lin: Research on vehicle license plate location based on neural networks. Proc. 1st I. Conf. on Innovative Computing, Information and Control, pp. 174-177, Beijing, 2006.
• [18] M. I. Chacon and A. Zimmerman: License plate location based on a dynamic PCNN scheme. Proc. Int. Joint Conf. Neural Netw., pp. 1195-1200, Portland, 2003.
• [19] K. K. Kim, K. I. Kim, J. B. Kim, and H. J. Kim: Learning-based approach, for license plate recognition. IEEE Signal Process. Soc. Workshop, NNs Signal Process. 2, 614-623 (2000).
• [20] M. H. ter Brugge, J. H. Stevens, J. A. G. Nijhuis, and L. Spaanenburg: License plate recognition using DTCNNs. in Proc. IEEE Int. Workshop Cellular NNs Applications, pp. 212-217, London, 1998.
• [21] X. Shi, W. Zhao, and Y. Shen: Automatic license plate recognition system based on colour image processing. LNCS 3483, 1159-1168 (2005).
• [22] N. Zimic, J. Ficzko, M. Mraz, and J. Virant: The fuzzy logic approach to the car number plate locating problem. in Proc. Adv. Soft Comp., pp. 227-230, The Bahamas, 1997.
• [23] S. L. Chang, L. S. Chen, Y. C. Chung, and S. W. Chen: Automatic license plate recognition. IEEE T. Intell. Transp. 5, 42-53, (2004).
• [24] F. Wang, L. Manb, B. Wang, Y. Xiao, W. Pan, and X. Lu: Fuzzy-based algorithm for colour recognition of license plates. Pattern Recogn. Lett.29, 1007-1020, (2008).
• [25] A. Daskalakis, D. Cavouras, P. Bougioukos, S. Kostopoulos, P.Georgiadis, I.Kalatzis, and G. Nikiforidis: An efficient CLAHE-based, spot-adaptive, image segmentation technique for improving microarray genes’ quantification. 2nd Int. Conf. on Experiments/Process/System Modelling/Simulation & Optimization, Athens, 2007.
• [26] M. Sepasian, C. Mares, S. M. Azimi, and W. Balachandran: Image enhancement for minutiae-based fingerprint identification. 37th IEEE Applied Imagery Pattern Recogn. Work- shop, pp.1-4, Washington, 2008.
• [27] R. C. Gonzalez and R. E. Woods, Digital Image Processing, PHI, Second Edition, 2006.
• [28] Z. Y. Che, B. R. Abidi, D. L. Page, and M. A. Abidi: Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement - Part I: the basic method. IEEE T. Image Process. 15, 123-135 (2006).
• [29] V. Abolghasemi and A. Ahmadyfard: An edge-based colour-aided method for license plate detection. Elsevier J. Image Vision Comput. 27, 1134-1142 (2009).
• [30] W. Jia, H. Zhang, and X. He: Region-based license plate detection. Elsevier J. Network Comput. Appl. 30, 1324-1333, (2007).
• [31] P. Wu, H.-H. Chen, R.-J. Wu, and D.-F. Shen: License plate extraction in low resolution video. Proc. 18th I. Conf. Pattern Recogn., pp. 824-827, Hong Kong, 2006.
• [32] N. Bellas, S. M. Chai, M. Dwyer, and D. Linzmeier: FPGA implementation of a license plate recognition SoC using automatically generated streaming accelerators. Proc. 20th Int. Parallel and Distributed Process. Sympos., pp. 8-15, Rhode Island, 2006.
• [33] H. Zhang, W. Jia, X. He, and Q. Wu: Real-time license plate detection under various conditions. Sciences 4159, 192-199, Springer-Verlag, Berlin, (2006).
• [34] S. Lin and J. J. Leou: A genetic algorithm approach to Chinese handwriting normalization. IEEE T. Syst. Man Cy. B27, 999-1006 (1997).
• [35] Y.-P. Huang, C.-H. Chen, Y.-T. Chang, and F.E. Sandnes: An intelligent strategy for checking the annual inspection status of motorcycles based on license plate recognition. Elsevier Expert Syst. Appl. 36, 9260-9267 (2009).
• [36] P. Clark and M. Mirmehdi: Combining statistical measures to find image text regions. 15th IEEE Int. Conf. Pattern Recogn., pp. 450-453, Barcelona, 2000.
• [37] C. N. Paunwala and S. Patnaik: License plate localization using bimodal attributes. Proc. Int. Conf. Comput. Vision, pp. 262-267, Vancouver, 2011.
• [38] A. Konar, Computational Intelligence: Principles, Techniques and Applications, Springer Publications, Berlin, 2005.
• [39] Y.-Q. Yang, J. Bai, R.-L. Tian, and N. Liu: A vehicle license plate recognition system based on fixed colour collocation. Proc. 4th Int. Conf. Mach. Learn. Cybernetics, pp. 5394-5397, Guangzhou, 2005.
• [40] http://www.medialab.ntua.gr/research/LPRdatabase.html (visited on 4/10/2011).
• [41] P. Kulkarni, A. Khatri, P. Banga, and K. Shah: A feature based approach for localization of Indian number plates. IEEE Int. Conf. Inform. Technol., pp. 157-162, Windsor, 2009.