Abstract: In this paper we present a modification to existed model of threshold for shot cut detection, which is able to adapt itself to the sequence statistics and operate in real time, because it use for calculation only previously evaluated frames. The efficiency of proposed modified adaptive threshold scheme was verified through extensive test experiment with several similarity metrics and achieved results were compared to the results reached by the original model. According to results proposed threshold scheme reached higher accuracy than existed original model.
Abstract: In this paper we present a novel method, which
reduces the computational complexity of abrupt cut detection. We
have proposed fast algorithm, where the similarity of frames within
defined step is evaluated instead of comparing successive frames.
Based on the results of simulation on large video collection, the
proposed fast algorithm is able to achieve 80% reduction of needed
frames comparisons compared to actually used methods without the
shot cut detection accuracy degradation.
Abstract: In this paper we present a method of abrupt cut detection with a novel logic of frames- comparison. Actual frame is compared with its motion estimated prediction instead of comparison with successive frame. Four different similarity metrics were employed to estimate the resemblance of compared frames. Obtained results were evaluated by standard used measures of test accuracy and compared with existing approach. Based on the results, we claim the proposed method is more effective and Pearson correlation coefficient obtained the best results among chosen similarity metrics.