The volume of cargo flowing through todays transportation system is growing at an ever increasing rate. Recent studies show that 90% of all international cargo that enters the United States flows through our vast seaport system. When this cargo enters the US, time is of the essence to quickly obtain and verify its identity, screen it against an ever increasingly wide variety of security concerns, and ultimately correctly direct the cargo towards its final destination.
Over the past few years, new port and container security initiatives and regulations have generated huge interest in the need for accurate real-time identification and tracking of incoming and outgoing traffic of vehicles and cargo. On the contrary, the manually intensive identification and tracking processes, typically employed today, are inherently both inefficient and inadequate, and can be seen as a possible enabling factor for potential threats to our ports and therefore our national security. The contradiction between current and required processes coupled to the correlation with accelerated growth in container traffic, has clearly identified the need for a solution.
One heavily researched option is the utilization of video based systems implementing Optical Character Recognition (OCR) processes for automatically extracting the unique container identification code to expedite the flow of cargo through various points in the seaport. The actual current process of how this occurs along with the opportunities and challenges for adding such a technological solution will be investigated in great detail.
This thesis will investigate the feasibility of application of motion compensation algorithms as an enhancement to OCR systems specifically designed to address the challenges of OCR of cargo containers in a seaport environment. This motion compensation could offer a cost effective alternative to the sophisticated hardware systems currently being offered to US ports.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/7137 |
| Date | 20 May 2005 |
| Creators | Siplon, Jonathan Page |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | 4119422 bytes, application/pdf |
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