Superframes, A Temporal Video Segmentation
Superframes, A Temporal Video Segmentation
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DOI：
10.1109/icpr.2018.8545723
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Hajar Sadeghi Sokeh;Vasileios Argyriou;D. Monekosso;Paolo Remagnino
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文献类型：
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作者：
Hajar Sadeghi Sokeh;Vasileios Argyriou;D. Monekosso;Paolo Remagnino
视频分割的目标是将视频数据转换成一组具体的运动簇，这些运动簇可以很容易地解释为视频的构件。已经有一些类似主题的工作，比如检测视频中的场景切换，但很少有关于将视频数据聚集到所需数量的紧凑片段中的具体研究。使用从低层分组过程中获得的有感知意义的实体将更直观、更高效，我们称之为“超帧”。提出了一种简单有效的视频内容模式相似超帧检测方法。我们计算内容-运动的相似度，以获得连续帧之间的变化强度。借助于现有的基于深度模型的光流技术，该方法能够更有效地进行更精确的运动估计。我们还提出了两个标准来衡量和比较不同算法在不同数据库上的性能。在Benchmark数据库视频上的实验结果证明了该方法的有效性。
The goal of video segmentation is to turn video data into a set of concrete motion clusters that can be easily interpreted as building blocks of the video. There are some works on similar topics like detecting scene cuts in a video, but there is few specific research on clustering video data into the desired number of compact segments. It would be more intuitive, and more efficient, to work with perceptually meaningful entity obtained from a low-level grouping process which we call it ‘superframe’. This paper presents a new simple and efficient technique to detect superframes of similar content patterns in videos. We calculate the similarity of content-motion to obtain the strength of change between consecutive frames. With the help of existing optical flow technique using deep models, the proposed method is able to perform more accurate motion estimation efficiently. We also propose two criteria for measuring and comparing the performance of different algorithms on various databases. Experimental results on the videos from benchmark databases have demonstrated the effectiveness of the proposed method.