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Muhammad Rizwan Asif

Performance Evaluation of Local Image Features for Multinational Vehicle License Plate Verification

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


  • Muhammad Rizwan Asif
  • Chun Qi, Xi'an Jiaotong University
  • ,
  • Irfana Bibi, Xi'an Jiaotong University
  • ,
  • Muhammad Sadiq Fareed, Xi'an Jiaotong University
  • ,
  • Zhe Zhang, Xi'an Jiaotong University
  • ,
  • Zhaoqiang Zhang, Xi'an Jiaotong University

The verification of vehicle License Plates (LPs) has not been given much importance in existing LP recognition systems as only a handful of methods deal with this problem explicitly. For an efficient system, it is imperative that a detected LP is validated first before the recognition of characters on it. Majority of the existing methods make use of geometrical constraints for the elimination of false LP regions which is not an effective way as multinational LPs have variable geometrical attributes and diversity in styles. To overcome these limitations, in this paper, we evaluate three kinds of representative local descriptors (SURF, HOG and LBP) and their combinations along with AIexNet CNN for the classification of LP and non-LP regions to provide a unique solution for the validation of multinational LPs. Experiments on 13490 LP and non-LP images show that the HOG feature individually gives the best recognition rate of 96.94% while considering collectively, best of 98.35% is achieved for SURF+HOG; whereas, the fine-tuned AIexNet outperform all others in terms of recognition accuracy of 99.27% but requires extensive processing. Furthermore, the proposed model is incorporated in one of the existing LP detection methods to demonstrate improved performance.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Vehicles Symposium, IV 2018
Number of pages6
PublisherInstitute of Electrical and Electronics Engineers Inc.
Publication year18 Oct 2018
Article number8500534
ISBN (Electronic)9781538644522
Publication statusPublished - 18 Oct 2018
Externally publishedYes
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: 26 Sep 201830 Sep 2018


Conference2018 IEEE Intelligent Vehicles Symposium, IV 2018
ByChangshu, Suzhou
SeriesIEEE Intelligent Vehicles Symposium, Proceedings

Bibliographical note

Funding Information:
*Research supported by the National Natural Science Foundation of China (Grant No. 61572395 and 61133008). †M.RizwanAsifiswithXi’anJiaotongUniversity,Xi’an,710049,P.R. China. He is also with COMSATS Institute of Information Technology, Lahore, 54000, Pakistan (e-mail: rizwansheikh123@hotmail.com). †Qi Chun is the corresponding author and is with Xi’an Jiaotong University,Xi’an,710049,P.R.China(e-mail: qichun@mail.xjtu.edu.cn).

Publisher Copyright:
© 2018 IEEE.

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