Abstract
Automatic license plate recognition (ALPR) methods and software are used in toll collection, traffic monitoring and other areas of road transport industry. Majority of ALPR methods and almost all in industrial use, try to recognize a license plate identifier from a single image. However, in a sequence of images, recognition of a license plate in any frame can be improved by considering the information from preceding and succeeding frames, using video object tracking. A new approach is presented, for combining a video tracking and a single frame ALPR method to improve the recognition rate. Unlike earlier techniques which are tied to specific object tracking and identifier recognition methods, the new method can be used with almost any tracking and single frame ALPR methods. Its key part is a method for clustering and alignment of candidate license plate identifiers in a video track. The results from five video sequences taken from a surveillance camera under various weather and light conditions demonstrate the recognition rate improvements.
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References
Donoser, M., Arth, C., Bischof, H.: Detecting, tracking and recognizing license plates. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 447–456. Springer, Heidelberg (2007)
Shahzad, A., Fraz, M., Elahi, M.A., Sarfraz, M.S.: Real time localization, tracking and recognition of vehicle license plate. In: VISAPP, pp. 685–688 (2011)
Dlagnekov, L., Belongie, S.: Recognizing cars. Technical report CS2005-083, CSE, UCSD (2005)
Matas, J., Zimmermann, K.: Unconstrained licence plate and text localization and recognition. In: Proceedings of the IEEE Intelligent Transportation Systems, pp. 225–230 (2005)
Mello, C.A.B., Costa, D.C.: A complete system for vehicle license plate recognition. In: WSSIpP, pp. 1–4 (2009)
Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (ALPR): a state-of-the-art review. IEEE Trans. Circ. Syst. Video Technol. 23, 311–325 (2013)
Jia, W., He, X., Piccardi, M.: Automatic license plate recognition: a review. In: Proceedings CISST, pp. 43–49 (2004)
Solanki, R., Kapadia, N., Patel, V.: The review on automatic license plate recognition (ALPR). IJRET 02, 178–183 (2013)
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., van den Hengel, A.: A survey of appearance models in visual object tracking. In: CoRR (2013). abs/1303.4803
Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: an experimental survey. Pattern Anal. Mach. Intell. 36, 1442–1468 (2013)
Milan, A., Leal-Taix, L., Schindler, K., Reid, I.: Joint Tracking and Segmentation of Multiple Targets CVPR (2015). https://bitbucket.org/amilan/segtracking
Staniszewski, M., Kloszczyk, M., Segen, J., Wereszczynski, K., Wojciechowski, K., Kulbacki, M.: Recent developments on tracking based methods (in preparation)
Donoser, M., Bischof, H.: Efficient maximally stable extremal region (MSER) tracking. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) pp. 553–560 (2006)
Zheng, L., He, X.: Number plate recognition based on support vector machines. In: Proceedings of the IEEE International Conference on Video and Signal Based Surveillance (AVSS06), p. 13. IEEE Computer Society, Washington (2006)
Openalpr.com: OpenALPR – Automatic License Plate Recognition. http://www.openalpr.com/
Acknowledgments
This work has been supported by the National Centre for Research and Development (project UOD-DEM-1-183/001 Intelligent video analysis system for behavior and event recognition in surveillance networks).
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Kluwak, K., Segen, J., Kulbacki, M., Drabik, A., Wojciechowski, K. (2016). ALPR - Extension to Traditional Plate Recognition Methods. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_73
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DOI: https://doi.org/10.1007/978-3-662-49390-8_73
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