Spatio-temporal video copy detection

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.