Content-Based Video Copy Detection, Tracking and Identification of Movie Pirates

Abstract

Due to the exponential growth of multimedia technologies, numerous pirated contents are proliferating on the Internet and causing huge piracy as well as copyright issues. Therefore, this thesis investigates four different methodologies for combating piracy namely, Content-Based video Copy Detection (CBCD), duplicate video registration, geometric distortions computation and pirate position estimation in a movie theater. In the first methodology, this thesis targets CBCD problem, by introducing different copy detection techniques, which employ efficient video fingerprints for detecting duplicate video clips. Precisely, this research work attempts to solve some of the issues of the CBCD domain, by proposing novel video copy detection techniques, that employ color, motion activity, audio and multimodal signatures. In the second methodology, this thesis addresses the problem of video copy localization, by proposing robust registration schemes, which guarantee the accurate frame alignments of the pirate video with the master content. Specifically, this research study contributes robust temporal as well as spatio-temporal registration frameworks, which exploit visual-audio fingerprints for obtaining frame-to-frame alignments of the two video sequences. In the third methodology, this thesis aims at geometric distortions estimation, by presenting a framework using visual-audio features, which computes the geometric distortions present in the duplicate video. In the fourth methodology, this thesis attempts to emphasize the capability of video fingerprints towards the pirate position estimation problem, by performing a Case study for investigating the illegal capture location in a theater. Video copy detection, tracking, distortion estimation and pirate position approximation frameworks presented in this thesis, are evaluated with extensive experiments on different datasets. More specifically, the experimental results demonstrate the efficiency of the proposed methods over standard datasets such as TRECVID dataset, Open Video Project dataset, CC WEB VIDEO collection and real datasets comprising camcorded versions of popular movies. Further, In-Theater experiments and evaluations demonstrate the satisfactory performance of the proposed forensic framework in terms of statistical results, 2D and 3D views of position estimations.