Finding and identifying unknown commercials using repeated video sequence detection
Computer Vision and Image Understanding
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Since the invention of television, and later the Internet, the amount of video content available has been growing rapidly. The great mass of visual material is an invaluable source of information, but its usefulness is limited by the available means of accessing and tailoring it to the needs of an individual. Long experience with text as a medium of conveying information allowed us to develop relatively effective methods of dealing with textual data. Unfortunately, the currently available techniques of accessing and processing video data are largely inadequate to the needs of its potential users. Hence video material remains a valuable but grossly untapped resource. In the domain of video news sources, this problem is especially severe. Television news stations broadcast continuous up-to-the-minute information from around the globe. For any individual viewer, only small portions of this news stream is of interest, yet currently no methods exist which would allow him to filter and monitor only the interesting news. In this dissertation, we demonstrate a solution to this problem by exploiting the repetitive character of the video news broadcast to create a story tracking algorithm. We observe that news stations often reuse the same video footage when reporting on the development of a story. We use this information to detect repetitions of the video footage and utilize this information for story tracking. To achieve this purpose in live video news broadcasts, we develop a real-time video sequence matching technique capable of identifying very short and only partially repeated sequences. We also introduce improvements in existing temporal video segmentation methods, which allow us to more accurately detect short video shots. The story tracking technique presented in this dissertation is complementary to existing textual topic detection and tracking methods and could be used in conjunction with them to improve the overall performance.