ROBUST NUMBER PLATE RECOGNITION IN IMAGE SEQUENCES

Andreas Zweng

2009

Abstract

License plate detection is done in three steps. The localization of the plate, the segmentation of the characters and the classification of the characters are the main steps to classify a license plate. Different algorithms for each of these steps are used depending on the area of usage. Corner detection or edge projection is used to localize the plate. Different algorithms are also available for character segmentation and character classification. A license plate is classified once for each car in images and in video streams, therefore it can happen that the single picture of the car is taken under bad lighting conditions or other bad conditions. In order to improve the recognition rate, it is not necessary to enhance character training or improve the localization and segmentation of the characters. In case of image sequences, temporal information of the existing license plate in consecutive frames can be used for statistical analysis to improve the recognition rate. In this paper an existing approach for a single classification of license plates and a new approach of license plate recognition in image sequences are presented. The motivation of using the information in image sequences and therefore classify one car multiple times is to have a more robust and converging classification where wrong single classifications can be suppressed.

References

  1. Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., et al. (2006). A license plate-recognition algorithm for intelligent transportation system applications. Intelligent Transport Systems, 7(3): pages 377-392.
  2. Feng Yang, Zheng Ma, Mei Xie, (2006). A novel approach for license plate character segmentation. In International Conference on Industrial Electronics and Applications, pages 1-6.
  3. Kim H., Kim J., (2000). Region-based shape descriptor invariant to rotation, scale and translation. Signal Process. Image Commun. 16 (2000) pages 87-93.
  4. Martinsky O., (2007). Algorithmic and Mathematical Principles of Automatic Number Plate Recognition Systems, B.SC Thesis, Brno.
  5. Peura M., Iivarinen J., (1997). Efficiency of simple shape descriptors. In Proceedings of the Third International Workshop on Visual Form, pages. 443-451.
  6. Rae Lee B., (2002). An active contour model for image segmentation: a variational perspective. In Proc. of IEEE International Conference on Acoustics Speech and Signal Processing, pages 1585-1588.
  7. Ridler T.W., Calvard S., (1987). Picture Thresholding Using an iterative Selection method. IEEE Trans. on Systems, Man, and Cybern., vol. 8, pages 630-632.
  8. Siti Norul Huda Sheikh A., Marzuki K., et al., (2007). Comparison of feature extractors in license plate recognition. In AMS 7807: Proceedings of the First Asia International Conference on Modelling & Simulation, pages 502-506.
  9. Xiao-Feng C., Bao-Chang P., Sheng-Lin Z., (2008). A license plate localization method based on region narrowing. Machine Learning and Cybernetics, 2008 International Conference, Volume 5, 12-15 July, pages 2700-2705.
  10. Zhang Y., Zhang C., (2003). New Algorithm for Character Segmentation of License Plate, Intelligent Vehicles Symposium, pages 106-109.
  11. Zhong Qin., Shengli Shi., Jianmin Xu., Hui Fu., (2006). Method of license plate location based on corner feature. In World Congress on Intelligent Control and Automation, pages 8645-8649.
  12. 1 http://www.cogvis.at
Download


Paper Citation


in Harvard Style

Zweng A. (2009). ROBUST NUMBER PLATE RECOGNITION IN IMAGE SEQUENCES . In Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009) ISBN 978-989-8111-68-5, pages 56-63. DOI: 10.5220/0001801200560063


in Bibtex Style

@conference{imagapp09,
author={Andreas Zweng},
title={ROBUST NUMBER PLATE RECOGNITION IN IMAGE SEQUENCES},
booktitle={Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)},
year={2009},
pages={56-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001801200560063},
isbn={978-989-8111-68-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)
TI - ROBUST NUMBER PLATE RECOGNITION IN IMAGE SEQUENCES
SN - 978-989-8111-68-5
AU - Zweng A.
PY - 2009
SP - 56
EP - 63
DO - 10.5220/0001801200560063