Browsing by Subject "Video recordings."
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Item Open Access Content based video copy detection using motion vectors(Bilkent University, 2009) Taşdemir, KasımIn this thesis, we propose a motion vector based Video Content Based Copy Detection (VCBCD) method. Detecting the videos violating the copyright of the owner comes into question by growing broadcasting of digital video on different media. Unlike watermarking methods in VCBCD methods, the video itself is considered as a signature of the video and representative feature parameters are extracted from a given video and compared with the feature parameters of a test video. Motion vectors of image frames are one of the signatures of a given video. We first investigate how well the motion vectors describe the video. We use Mean value of Magnitudes of Motion Vectors (MMMV) and Mean value of Phases of Motion Vectors (MPMV) of macro blocks, which are the main building blocks of MPEG-type video coding methods. We show that MMMV and MPMV plots may not represent videos uniquely with little motion content because the average of motion vectors in a given frame approaches zero. To overcome this problem we calculate the MMMV and MPMV graphs in a lower frame rate than the actual frame rate of the video. In this way, the motion vectors may become larger and as a result robust signature plots are obtained. Another approach is to use the Histogram of Motion Vectors (HOMV) that includes both MMMV and MPMV information. We test and compare MMMV, MPMV and HOMV methods using test videos including copies and the original movies.Item Open Access Content-based video copy detection using multimodal analysis(Bilkent University, 2009) Küçüktunç, OnurHuge and increasing amount of videos broadcast through networks has raised the need of automatic video copy detection for copyright protection. Recent developments in multimedia technology introduced content-based copy detection (CBCD) as a new research field alternative to the watermarking approach for identification of video sequences. This thesis presents a multimodal framework for matching video sequences using a three-step approach: First, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In the second step, a spatiotemporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Finally the non-facial shots are matched using low-level visual features. In addition, we utilize fuzzy logic approach for extracting color histogram to detect shot boundaries of heavily manipulated video clips. Methods for detecting noise, frame-droppings, picture-in-picture transformation windows, and extracting mask for still regions are also proposed and evaluated. The proposed method was tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results were compared with the results of top-8 most successful techniques submitted to this task. Experimental results show that the proposed method performs better than most of the state-of-the-art techniques, in terms of both effectiveness and efficiency.Item Open Access Detection and tracking of repeated sequences in videos(Bilkent University, 2007) Can, TolgaIn this thesis, we propose a new method to search different instances of a video sequence inside a long video. The proposed method is robust to view point and illumination changes which may occur since the sequences are captured in different times with different cameras, and to the differences in the order and the number of frames in the sequences which may occur due to editing. The algorithm does not require any query to be given for searching, and finds all repeating video sequences inside a long video in a fully automatic way. First, the frames in a video are ranked according to their similarity on the distribution of salient points and colour values. Then, a tree based approach is used to seek for the repetitions of a video sequence if there is any. These repeating sequences are pruned for more accurate results in the last step. Results are provided on two full length feature movies, Run Lola Run and Groundhog Day, on commercials of TRECVID 2004 news video corpus and on dataset created for CIVR Copy Detection Showcase 2007. In these experiments, we obtain %93 precision values for CIVR2007 Copy Detection Showcase dataset and exceed %80 precision values for other sets.