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dc.contributor.advisorAlom, Md. Zahangir
dc.contributor.advisorKarim, Risul
dc.contributor.authorHossen, Khalid
dc.contributor.authorMahmud, Hasan
dc.date.accessioned2014-05-14T05:13:30Z
dc.date.available2014-05-14T05:13:30Z
dc.date.copyright2014
dc.date.issued2014-04-30
dc.identifier.otherID 10101019
dc.identifier.otherID 10101026
dc.identifier.urihttp://hdl.handle.net/10361/3224
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2014.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 30).
dc.description.abstractThe intention of this thesis paper is to deploy a parallel implementation of the optical flow detection algorithm known as the Lucas-Kanade algorithm. As an important algorithm in the field of computer vision, it is believed that it holds much promise and shows much potential for benefiting from techniques used to enhance performance through parallel programming which can be executed with the use of CUDA. Though more techniques of parallel programming exist that can be used to fasten the process, Lucas-Kanade has never been implemented in parallel programming before. The result of the research has shown both serial and parallel implementation of optical flow detection using deferent processing units (CPUs and GPUs). The parallel implementation have lessened 2 to 13 seconds of processing time (depending on the hardware configuration) for the same database compare to serial implementation.en_US
dc.description.statementofresponsibilityKhalid Hossen
dc.description.statementofresponsibilityHasan Mahmud
dc.format.extent31 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectComputer science and engineeringen_US
dc.titleParallel optical flow detection using CUDAen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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