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Traffic density estimation and flow control for video surveillance system

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dc.contributor.advisor Hammad Ali, Abu Mohammad
dc.contributor.advisor Alam, Md. Zahangir
dc.contributor.author Paul, Partho Sharothi
dc.date.accessioned 2014-08-27T09:24:47Z
dc.date.available 2014-08-27T09:24:47Z
dc.date.copyright 2014
dc.date.issued 2014
dc.identifier.other ID 09101014
dc.identifier.uri http://hdl.handle.net/10361/3486
dc.description This 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.description Cataloged from PDF version of thesis report.
dc.description Includes bibliographical references (page 27 - 28).
dc.description.abstract Video monitoring and surveillance have been widely used in traffic surveillance system. It is important to know the road traffic density in predefined traffic videos especially in mega cities like Dhaka for signal control and effective traffic management .In this paper, I researched on vehicle density estimation and flow control for outdoor traffic surveillance is presented. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differ significantly from a background model. For Background modeling I used Frame differencing method as for density estimation as our background is static. My experiments shows static background subtraction algorithms with adaptive thresholding, post-processing with morphological image processing can produce good results with much lower computational complexity. Depending on the number of vehicle my embedded system will generate signal effectively to Control the flow of traffic in the road. en_US
dc.description.statementofresponsibility Partho Sharothi Paul
dc.format.extent 28 pages
dc.language.iso en en_US
dc.publisher BRAC University en_US
dc.rights BRAC 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.subject Computer science and engineering en_US
dc.subject Background segmentation en_US
dc.subject Image processing en_US
dc.subject Density estimation en_US
dc.subject Embedded system en_US
dc.subject Traffic signal system en_US
dc.title Traffic density estimation and flow control for video surveillance system en_US
dc.type Thesis en_US
dc.contributor.department Department of Computer Science and Engineering, BRAC University
dc.description.degree B. Computer Science and Engineering


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