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Spatio-temporal video copy detection

Resource type
Thesis type
((Thesis)) M.Sc.
Date created
2011-09-15
Authors/Contributors
Abstract
Video Copy Detection is used to detect copies of original content. Features of the content are used to create a unique and compact description of the video. We present a video copy detection system which capitalizes on the discriminating ability of Speeded Up Robust Features (SURF) to find points of interest. We divide selected frames into regions and count the points within each region. This spatial signature is given a temporal component by ranking the counts along the time line. The signature requires just 16 bytes per frame. It was evaluated using TRECVID’s 2009 dataset comprising over 180 hours of video content. The system could detect copies transformed to the extreme limits of TRECVID’s evaluation criteria. These transforms included changing contrast, resizing, changing gamma values, flipping, rotating, shifting, cropping, blurring, stretching, zooming, camcording, and text or pattern insertion. The proposed system is also computationally efficient.
Document
Identifier
etd6860
Copyright statement
Copyright is held by the author.
Permissions
The author granted permission for the file to be printed and for the text to be copied and pasted.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Hefeeda, Mohamed
Member of collection
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etd6860_RHarvey.pdf 1.94 MB

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