Hsin-Fu Chen, Chang-Yun Chiang, Shih-Jui Yang, C. C. Ho
{"title":"Android-based patrol robot featuring automatic license plate recognition","authors":"Hsin-Fu Chen, Chang-Yun Chiang, Shih-Jui Yang, C. C. Ho","doi":"10.1109/COMCOMAP.2012.6154014","DOIUrl":null,"url":null,"abstract":"This work develops an Android-based robot featuring automatic license plate recognition and automatic license plate patrolling. The automatic license plate recognition feature combines 4 self-developed novel methods, Wiener-deconvolution vertical edge enhancement, AdaBoost plus vertical-edge license plate detection, vertical edge projection histogram segmentation stain removal, and customized optical character recognition. Besides, the automatic license plate patrolling feature also integrates 3 novel methods, HL2-band rough license plate detection, orientated license plate approaching, and Ad-Hoc-based remote motion control. Implementation results show the license plate detection rate and recognition rate of the Android-based robot are over 99% and over 98%, respectively, under various scene conditions. Especially, the execution time of license plate recognition, including license plate detection, is only about 0.7 second per frame on the Android-based robot.","PeriodicalId":281865,"journal":{"name":"2012 Computing, Communications and Applications Conference","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Computing, Communications and Applications Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMCOMAP.2012.6154014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
This work develops an Android-based robot featuring automatic license plate recognition and automatic license plate patrolling. The automatic license plate recognition feature combines 4 self-developed novel methods, Wiener-deconvolution vertical edge enhancement, AdaBoost plus vertical-edge license plate detection, vertical edge projection histogram segmentation stain removal, and customized optical character recognition. Besides, the automatic license plate patrolling feature also integrates 3 novel methods, HL2-band rough license plate detection, orientated license plate approaching, and Ad-Hoc-based remote motion control. Implementation results show the license plate detection rate and recognition rate of the Android-based robot are over 99% and over 98%, respectively, under various scene conditions. Especially, the execution time of license plate recognition, including license plate detection, is only about 0.7 second per frame on the Android-based robot.