Robust license plate detection using image saliency

Kai Hsiang Lin, Hao Tang, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inspired from the observation that license plates are very salient to human visual perception, we propose a novel license plate detection algorithm based on image saliency in this paper. The proposed algorithm consists of two parts. The first part segments out the characters on a license plate using an intensity saliency map with a high recall rate. The second part applies a sliding window on these characters to compute some saliency-related features to detect license plates. We test the robustness of our algorithm by applying it on a mixed data set with high diversity collected from four databases. The mixed data set has 1024 images composed of license plates of all states of the U.S. We achieve a detection rate of 90% with False Positive Per Image (FPPI) = 12%. The detection box given by our algorithm has high precision, which will be very helpful for many applications such as license plate recognition.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages3945-3948
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period9/26/109/29/10

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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