Efficient copy detection for compressed digital videos by spatial and temporal feature extraction

This research aims at developing a practical video copy detection mechanism to determine whether an investigated video is a duplicated copy that may infringe the intellectual property rights. The significant features of original videos are extracted and stored in the server. Given an uploaded video, the same feature is extracted and compared with the stored ones to seek a possible match. Both the spatial and temporal features of compressed videos are employed in the proposed scheme. The scene-change detection is applied to select the key frames, from which the robust spatial features are extracted to help search visually similar frames. The shot lengths are used as the temporal features to further ensure the matching accuracy. To ensure that the proposed method is practical in considered applications, the size of stored features in the server, efficiency and accuracy of matching features are the major design principles. The experimental results by testing a large number of compressed videos demonstrate the feasibility of the proposed scheme.