Shot transition detection

It is also a fundamental step of automated indexing and content-based video retrieval or summarization applications which provide an efficient access to huge video archives, e.g. an application may choose a representative picture from each scene to create a visual overview of the whole film and, by processing such indexes, a search engine can process search items like "show me all films where there's a scene with a lion in it."

In simple terms cut detection is about finding the positions in a video in that one scene is replaced by another one with different visual content.

Technically speaking the following terms are used: A digital video consists of frames that are presented to the viewer's eye in rapid succession to create the impression of movement.

Cut detection would be a trivial problem if each frame of a video was enriched with additional information about when and by which camera it was taken.

In the decision phase the following approaches are usually used: All of the above algorithms complete in O(n) — that is to say they run in linear time — where n is the number of frames in the input video.

Automatic shot transition detection was one of the tracks of activity within the annual TRECVid benchmarking exercise from 2001 to 2007.

The benchmark has compared 6 methods on more than 120 videos from RAI and MSU CC datasets with different types of scene changes, some of which were added manually.