A new license plate extraction framework based on fast mean shift

Luning Pan; Shuguang Li

doi:10.1117/12.867250

19 August 2010 A new license plate extraction framework based on fast mean shift

Luning Pan, Shuguang Li

Proceedings Volume 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering; 782007 (2010) https://doi.org/10.1117/12.867250
Event: International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 2010, Xi'an, China

Abstract

License plate extraction is considered to be the most crucial step of Automatic license plate recognition (ALPR) system. In this paper, a region-based license plate hybrid detection method is proposed to solve practical problems under complex background in which existing large quantity of disturbing information. In this method, coarse license plate location is carried out firstly to get the head part of a vehicle. Then a new Fast Mean Shift method based on random sampling of Kernel Density Estimate (KDE) is adopted to segment the color vehicle images, in order to get candidate license plate regions. The remarkable speed-up it brings makes Mean Shift segmentation more suitable for this application. Feature extraction and classification is used to accurately separate license plate from other candidate regions. At last, tilted license plate regulation is used for future recognition steps.

Citation Download Citation

Luning Pan and Shuguang Li "A new license plate extraction framework based on fast mean shift", Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 782007 (19 August 2010); https://doi.org/10.1117/12.867250

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