Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Automatic partitioning of full-motion video
Multimedia Systems
Metrics for shot boundary detection in digital video sequences
Multimedia Systems
Automatic Video Indexing and Full-Video Search for Object Appearances
Proceedings of the IFIP TC2/WG 2.6 Second Working Conference on Visual Database Systems II
A New Shot Boundary Detection Algorithm
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Scene change detection using the weighted chi-test and automatic threshold decision algorithm
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part IV
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
Performance characterization of video-shot-change detection methods
IEEE Transactions on Circuits and Systems for Video Technology
A robust scene-change detection method for video segmentation
IEEE Transactions on Circuits and Systems for Video Technology
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Flashlights in video cause abrupt brightness changes of a scene and will be detected as false scene change if not handled properly. So in this paper propose a robust scene change detection algorithm which can detect the scene change correctly by skipping for the flashing period. At first, the proposed methods make use of histogram comparison which are simple and more robust to object and camera movement while enough spatial information is retained to produce more accurate difference values from consecutive frames. The normalized works of difference values are performed to solve the optimal threshold decision problem. Normalized difference values are dynamically compressed by Log metrics and more efficient to detect scene boundary. Finally, we distinguish flashlights from difference values by applying a 'flashlights features' which are defined based on the temporal property of normalized difference values across a frame sequence. The proposed methods are tested on the various video types and experimental results show that the proposed algorithms are effective and reliably detect scene changes.