VIDEO SHOT BOUNDARY DETECTION(17)

BOUNDARY DETECTION(17)

Block matching is used to retrieve an initial estimate of the image displacement. To obtain a dense displacement field, matching with adaptive block sizes was implemented. In this typical algorithm, a frame is divided into blocks of M x N pixels or, more usually, square blocks of N2 pixels. Then, we assume that each block undergoes translation only with no scaling or rotation. The blocks in the first frame are compared to the blocks in the second frame. Motion Vectors can then be calculated for each block to see where each block from the first frame ends up in the second frame. For every video sequence we determine the number of shots, the number of shots correctly reported, the number of false detections and the number of non reported shots.

For each sequence we also draw the curve of the distances between the successive frames. These curves are mainly used to determine the threshold values, but they also give an idea about the capacity of the used technique in detecting transitions.

GIST method

In this method we apply gist and color features to represent the individual frames. Gist has been shown to characterize the structure of images well while being resistant to luminance change and also small translation. Thus, it is ideally suited for our application of shot boundary detection. The most intuitive way to detect shot change is to compute the pixel difference of two consecutive frames, if the overall difference is larger than some threshold, it is detected as a shot boundary.

Representative APR 391%

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