In the last decade, several effective algorithms have been developed in the areas of content based video retrieval and copy detection. However, the task of video copy detection becomes more difficult when the target video sequences available are in degraded or edited formats. In this thesis, we propose an improved mechanism to detect the copy of video clip in the real time video stream as well as in offline videos under edited or degraded circumstances. Our algorithm gives encouraging results for detecting the possible copy in the target videos with spatial as well as temporal alterations. Spatial alterations can include addition of text/subtitles or logos or other static objects to the video while the temporal alterations include insertion of occasional title frames, images or short video clips according to the esthetics of the video editor. Also, the appearance of video frames in an arbitrary order has also been found an issue of temporal degradation in broadcasted videos. This issue has been given consideration too.
Since our algorithm analyzes the difference of ordinal measurements of reference and target videos at each frame level, so it not only attempts to detect the replica of whole reference video clip but can also specify its partial replica in spatial as well as temporal direction. That is, it can detect the target video frames which have been copied even if the frames had some spatial editing. Furthermore, several experiments have been conducted with various sizes of rank matrices obtained from video frame partitions in order to minimize the false copy detection of the videos. Various kinds of video benchmarks have been used to obtain the statistical measurements. By using these statistical measurements, we inferred the preferable length of benchmark frames of reference video, and the generation of rank matrices and their optimal sizes.