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作者:

Nie, Wenzhen (Nie, Wenzhen.) | Liu, Pengyu (Liu, Pengyu.) | Jia, Kebin (Jia, Kebin.) (学者:贾克斌) | Liao, Huimin (Liao, Huimin.) | Huang, Xunping (Huang, Xunping.)

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EI Scopus

摘要:

This article aims at Beijing Traffic Enforcement Corps off-site law enforcement issues. This paper proposes a novel detection method for taxi license plate block. First of all, selecting the taxi from the social vehicle; Secondly, Adaptive Boosting (Adaboost) algorithm will be used to train the license plate to locate the license plate of the taxi; eventually the adaptive threshold method will be used to judge the license plate blockage and take the evidence. The existing research on the license plate is mainly on the license plate recognition, but this article is based on the license plate recognition, and then to achieve the license plate block detection and evidence collection, for traffic law enforcement officers to punish the illegal taxi. The experimental results show that the proposed detection method of license plate block is effective. © 2018 IEEE.

关键词:

Adaptive boosting Law enforcement License plates (automobile) Optical character recognition Taxicabs

作者机构:

  • [ 1 ] [Nie, Wenzhen]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Nie, Wenzhen]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 3 ] [Nie, Wenzhen]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Nie, Wenzhen]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Liu, Pengyu]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Liu, Pengyu]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 7 ] [Liu, Pengyu]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 8 ] [Liu, Pengyu]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Jia, Kebin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 10 ] [Jia, Kebin]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 11 ] [Jia, Kebin]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 12 ] [Jia, Kebin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 13 ] [Liao, Huimin]Traffic Execution BR Gade of Beijing, China
  • [ 14 ] [Huang, Xunping]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 15 ] [Huang, Xunping]Beijing Laboratory of Advanced Information Networks, Beijing; 100124, China
  • [ 16 ] [Huang, Xunping]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 17 ] [Huang, Xunping]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China

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来源 :

年份: 2018

页码: 81-85

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 6

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 3

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