Abstract
This paper introduces a novel real-time framework which enables detection, tracking and recognition of license plates from video sequences. An efficient algorithm based on analysis of Maximally Stable Extremal Region (MSER) detection results allows localization of international license plates in single images without the need of any learning scheme. After a one-time detection of a plate it is robustly tracked through the sequence by applying a modified version of the MSER tracking framework which provides accurate localization results and additionally segmentations of the individual characters. Therefore, tracking and character segmentation is handled simultaneously. Finally, support vector machines are used to recognize the characters on the plate. An experimental evaluation shows the high accuracy and efficiency of the detection and tracking algorithm. Furthermore, promising results on a challenging data set are presented and the significant improvement of the recognition rate due to the robust tracking scheme is proved.
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.
Similar content being viewed by others
References
Shapiro, V., Gluhchev, G., Dimov, D.: Towards a multinational car license plate recognition system 17(3), 173–183 (2006)
Jia, W., Zhang, H., He, X., Piccardi, M.: Mean shift for accurate license plate localization. In: ITSC. Proceedings of the IEEE Conference on Intelligent Transportation Systems, pp. 566–571. IEEE Computer Society Press, Los Alamitos (2005)
Dlagnekov, L., Belongie, S.: Recognizing cars. Technical Report CS2005-0833, UCSD University of California, San Diego (2005)
Matas, J., Zimmermann, K.: Unconstrained licence plate and text localization and recognition. In: ITSC. Proceedings of the IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, pp. 572–577. IEEE Computer Society Press, Los Alamitos (2005)
Rahman, C., Badawy, W., Radmanesh, A.: A real time vehicle’s license plate recognition system. In: AVSS. Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 163–166. IEEE Computer Society Press, Los Alamitos (2003)
Matas, J., Zimmermann, K.: Unconstrained licence plate detection. In: ITSC. Proceedings of International Conference on Intelligent Transportation Systems, pp. 572–577 (2005)
Donoser, M., Bischof, H.: Efficient maximally stable extremal region (MSER) tracking. In: CVPR. Proceedings of Conference on Computer Vision and Pattern Recognition, pp. 553–560 (2006)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC. Proceedings of British Machine Vision Conference, pp. 384–393 (2002)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 27(10), 1615–1630 (2005)
Zhang, H., Jia, W., He, X., Wu, Q.: Learning-based license plate detection using global and local features. In: ICPR. Proceedings of International Conference on Pattern Recognition, pp. 1102–1105 (2006)
Najman, L., Couprie, M.: Quasi-linear algorithm for the component tree. In: SPIE Vision Geometry XII, vol. 5300, pp. 98–107 (2004)
Matas, J., Zimmermann, K.: A new class of learnable detectors for categorisation. In: SCIA. Proceedings of Scandinavian Conference of Image Analysis, pp. 541–550 (2005)
Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, NY, USA (1995)
Zheng, L., He, X.: Number plate recognition based on support vector machines. In: AVSS 2006. Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, Washington, DC, USA, p. 13. IEEE Computer Society Press, Los Alamitos (2006)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Donoser, M., Arth, C., Bischof, H. (2007). Detecting, Tracking and Recognizing License Plates. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_44
Download citation
DOI: https://doi.org/10.1007/978-3-540-76390-1_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76389-5
Online ISBN: 978-3-540-76390-1
eBook Packages: Computer ScienceComputer Science (R0)