A novel license plate location method based on wavelet transform and EMD analysis

被引:78
作者
Yu, Shouyuan [1 ,2 ]
Li, Baopu [2 ,4 ]
Zhang, Qi [2 ]
Liu, Changchun [1 ]
Meng, Max Q. -H. [3 ]
机构
[1] Shandong Univ, Sch Control Sci & Engn, Jinan 250100, Peoples R China
[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
[3] Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
[4] Shenzhen Univ, Dept Biomed Engn, Shenzhen, Peoples R China
基金
中国国家自然科学基金;
关键词
License plate location; Wavelet transform; EMD analysis; Hilbert transform; EMPIRICAL MODE DECOMPOSITION; RECOGNITION; ALGORITHM; VEHICLES;
D O I
10.1016/j.patcog.2014.07.027
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Although various license plate location methods have been proposed in the past decades, their accuracy and ability to deal with different types of license plates still need to be improved. A robust license plate location method can raise the accuracy of the whole license plate recognition procedure. This paper proposes a robust method based on wavelet transform and empirical mode decomposition (EMD) analysis to search for the location of a license plate in an image to deal with some challenging problems in practice such as illumination changes, complex background and perspective change. By applying wavelet transform on a vehicle image and projecting the acquired details of the image, a wave crest that indicates the license plate will be generated. In order to locate the desired wave crest in the nonlinear and non-stationary projection dataset, EMD analysis is applied. Using the reconstructed projection data and the Hilbert transform of intrinsic mode function components, the position of the license plate is detected. Comprehensive experiments show that this method can locate the positions of various types of license plates with a high accuracy of 97.91% and a relatively short running time. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:114 / 125
页数:12
相关论文
共 30 条
  • [11] License plate localization and character segmentation with feedback self-learning and hybrid binarization techniques
    Guo, Jing-Ming
    Liu, Yun-Fu
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2008, 57 (03) : 1417 - 1424
  • [12] Hongyuan Wang, 2010, Proceedings of the 2010 International Conference on Electrical and Control Engineering (ICECE 2010), P1234, DOI 10.1109/iCECE.2010.308
  • [13] Huang DJ, 2003, ACTA OCEANOL SIN, V22, P1
  • [14] Huang N. E, 2004, US Patent, Patent No. 6738734
  • [15] The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
    Huang, NE
    Shen, Z
    Long, SR
    Wu, MLC
    Shih, HH
    Zheng, QN
    Yen, NC
    Tung, CC
    Liu, HH
    [J]. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 1998, 454 (1971): : 903 - 995
  • [16] Huang NE, 2005, INTERD MATH SCI, V5, P1
  • [17] Critical Scenarios and Their Identification in Parallel Railroad Level Crossing Traffic Control Systems
    Huang, Yi-Sheng
    Weng, Yi-Shun
    Zhou, MengChu
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2010, 11 (04) : 968 - 977
  • [18] License plate detection based on expanded haar wavelet transform
    Hung, Kuo-Ming
    Chuang, Hsiang-Lin
    Hsieh, Ching-Tang
    [J]. FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 4, PROCEEDINGS, 2007, : 415 - +
  • [19] A configurable method for multi-style license plate recognition
    Jiao, Jianbin
    Ye, Qixiang
    Huang, Qingming
    [J]. PATTERN RECOGNITION, 2009, 42 (03) : 358 - 369
  • [20] Kim KK, 2000, NEURAL NETWORKS FOR SIGNAL PROCESSING X, VOLS 1 AND 2, PROCEEDINGS, P614, DOI 10.1109/NNSP.2000.890140