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Conference Paper: A vision-based optical character recognition system for real-time identification of tractors in a port container terminal

TitleA vision-based optical character recognition system for real-time identification of tractors in a port container terminal
Authors
Issue Date2012
PublisherCOC Publications, Curtin University.
Citation
The 5th International Conference on Optimization and Control with Applications, Beijing, China, 4-8 December 2012. In Conference Proceedings, 2012, p. 241, abstract no. 79 How to Cite?
AbstractAutomation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.
Persistent Identifierhttp://hdl.handle.net/10722/189929

 

DC FieldValueLanguage
dc.contributor.authorChau, Den_US
dc.contributor.authorChau, Jen_US
dc.contributor.authorLau, HYKen_US
dc.date.accessioned2013-09-17T15:03:07Z-
dc.date.available2013-09-17T15:03:07Z-
dc.date.issued2012en_US
dc.identifier.citationThe 5th International Conference on Optimization and Control with Applications, Beijing, China, 4-8 December 2012. In Conference Proceedings, 2012, p. 241, abstract no. 79en_US
dc.identifier.urihttp://hdl.handle.net/10722/189929-
dc.description.abstractAutomation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.-
dc.languageengen_US
dc.publisherCOC Publications, Curtin University.-
dc.relation.ispartofProceedings of the 5th International Conference on Optimization and Control with Applications, OCA2012en_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA vision-based optical character recognition system for real-time identification of tractors in a port container terminalen_US
dc.typeConference_Paperen_US
dc.identifier.emailChau, J: jwcc@hku.hken_US
dc.identifier.emailLau, HYK: hyklau@hkucc.hku.hken_US
dc.identifier.authorityLau, HYK=rp00137en_US
dc.description.naturepostprint-
dc.identifier.hkuros222506en_US
dc.identifier.spage241-
dc.identifier.epage241-
dc.publisher.placeAustralia-

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