Abstract

In this paper, a novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on a compressed multimedia bitstream is proposed. Scene change detection is crucial in that it enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. We use statistical sequential analysis to provide a unified framework for robust and effective detection of both abrupt and gradual scene changes.

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