Taxi License Plate Block Detection Based on Complex Environment - Details

Author：

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

Indexed by：

EI Scopus

Abstract：

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.

Keyword：

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

Author Community：

[ 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

Reprint Author's Address：

Show more details

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Source ：

Year： 2018

Page： 81-85

Language： English

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count： 7

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 2

Affiliated Colleges：

信息学部

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