Comparison of automatic shot boundary detection algorithms

Various methods of automatic shot boundary detection have been proposed and claimed to perform reliably. Detection of edits is fundamental to any kind of video analysis. It segments a video into its basic components, that is, the shots. However, only few comparative investigations on early shot boundary detection algorithms have been published. These investigations mainly concentrate on measuring the edit detection performance. However, they do not consider the algorithms' ability to classify the types, and to locate the boundaries of the edits correctly. This paper extends these comparative investigations. More recent algorithms designed explicitly to detect specific complex editing operations, such as fades and dissolves, are taken into account. In addition, their ability to classify the types and locate the boundaries of such edits are examined. The algorithms' performance is measured in terms of hit rate, number of false hits, and miss rate for hard cuts, fades, and dissolves, over a large and diverse set of video sequences. The experiments show that while hard cuts and fades can be detected reliably, dissolves are still an open research issue. The false hit rate for dissolves is usually unacceptably high, ranging from 50 percent up to more than 400 percent. Moreover, all algorithms seem to fail under roughly the same conditions.