Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Few published studies compare available algorithms, and those that do have looked at limited range of test material. A comparison of several shot boundary detection and classification techniques can be done using their variations including histograms, discrete cosine transform, motion vector, and block matching methods. The performance and ease of selecting good thresholds for these algorithms are evaluated based on a wide variety of video sequences with a good mix of transition types. Threshold selection requires a trade-off between recall and precision that must be guided by the target application.

Video shot boundary detection using Color Histogram

The approach here takes each (352 * 288) frame of the video and computes a color histogram using a total of 192 different colour bins where each bin contains the percentage of pixels from the whole frame. When this is done, the color histogram for each frame is checked against the histogram for the one following and a similarity metric is used to compute the likeness between the two adjacent frames. When the sequence of similarity values is analysed, a shot will show a big change in the sequence where the shot boundary occurs, so by looking for these peaks in the differences, a shot boundary can be detected. The peak in the values is required to be above some minimum value before it will be detected as a shot cut.

Representative APR 391%

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