Robust Frame Duplication Detection for Degraded Videos

Figure 3

The comparison between the three methods for image alignment. Top row: (a, b) a frame and its copy. Bottom row: (a, b) two consecutive frames. The video is recorded by using a still camera. The person in a black coat is walking toward left, and the person’s gesture slightly changed across the two frames. (c)–(e) In both rows, the offset fields calculated based on optical flow [25] (we use the implementation in [27]), SIFT flow [23], and our method, respectively. Despite the impressive result for the motion of the walking person, optical flow is too sensitive to perturbations caused by lossy compression; it gets nonzero offsets all over the fields for both cases, particularly for the wall region in the first row. SIFT flow obtains better result than optical flow for the first case, but there is still a large area of nonzero values corresponding to the smooth wall. On the contrary, SIFT flow is not sensitive enough to subtle changes: it fails to detect the motion of the person in the second case. In contrast, our method correctly calculates both offset fields.

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