A Smooth Video Summarization Method Based on Frame-Filling

Given the existing video summarization algorithms have too much redundancy in video semantics, and destroy the maximum continuity between video frames, a smooth video summarization generation method is designed in this paper. This method is based on the video keyframe selection, which is mainly divided into three parts. In the first part, this method completes the de-redundancy operation based on the similarity calculated by the essential characteristics of the video frame. The second part completes the shot segmentation processing using the capsule network. The third part can be divided into two sub-parts, key frames selection and filling. The former of this part is selects key-frames based on video semantic, and the latter is smooths summarization based on frame-filling. Experimental results show that the video summarization algorithms designed in this paper can outperform several existing algorithms and achieve excellent results in terms of video content integrity, semantic accuracy and so on.