Abstract
An embedded hardware for license plate character recognition is designed and implemented on an FPGA (field programmable gate array) with pipeline architecture. The architecture is based on M2DPCA (modular two-dimensional principal component analysis) algorithm. Three processing elements are contained in the proposed pipeline architecture, projection element is designed for matrix multiplication operations of feature extraction, the distances between input character and each class in training database are computed in distance element, and the nearest neighbor classification is carried out in classification element, all functions are run in pipeline. Experimental results show that very high speed is achieved, which provides approximately 28% speedup of equivalent software implementation, and also, the hardware architecture performs extremely resource economical.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Huang, Y.S., Weng, Y.S., Zhou, M.C.: Critical scenarios and their identification in parallel railroad level crossing traffic control systems. IEEE Transactions on Intelligent Transportation Systems 11(4), 968–977 (2010)
Omitaomu, O.A., Ganguly, A.R., Patton, B.W., Protopopescu, V.A.: Anomaly detection in radiation sensor data with application to transportation security. IEEE Transactions on Intelligent Transportation Systems 10(2), 324–334 (2009)
Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Psoroulas, I.D., Loumos, V., Kayafas, E.: License plate recognition from still images and video sequences: A survey. IEEE Transactions on Intelligent Transportation Systems 9(3), 377–391 (2008)
Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Trans. Pattern Analysis and Machine Intelligence 12(1), 103–108 (1990)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Zhang, D., Mabu, S., Hirasawa, K.: Robust intelligent PCA-based face recognition framework using GNP-fuzzy data mining. IEEJ Transactions on Electrical and Electronic Engineering 8(3), 253–262 (2013)
Gottumukkal, R., Asari, V.K.: An improved face recognition technique based on modular PCA approach. Pattern Recognition Letters 25(4), 429–436 (2004)
Yang, J., Zhang, D., Frangi, A.F.: Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 26(1), 131–137 (2004)
Chen, F., Chen, X., Zhang, S., Yang, J.: A Human Face Recognition Method Based on Modular 2DPCA. Image and Graphics 11(4), 580–585 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gu, B., Zhang, Q., Zhao, Z. (2014). Pipeline Architecture for High Speed License Plate Character Recognition. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Huang, K. (eds) Advances in Image and Graphics Technologies. IGTA 2014. Communications in Computer and Information Science, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45498-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-662-45498-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45497-8
Online ISBN: 978-3-662-45498-5
eBook Packages: Computer ScienceComputer Science (R0)