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A Novel Key-Frames Selection Framework for Comprehensive Video Summarization

机译：全面视频摘要的新型密钥框选择框架

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摘要

Video summarization (VSUMM) has become a popular method in processing massive video data. The key point of VSUMM is to select the key frames to represent the effective contents of a video sequence. The existing methods can only extract the static images of videos as the content summarization, but they ignore the representation of motion information. To cope with these issues, a novel framework for an efficient video content summarization as well as video motion summarization is proposed. Initially, Capsules Net is trained as a spatiotemporal information extractor, and an inter-frames motion curve is generated based on those spatiotemporal features. Subsequently, a transition effects detection method is proposed to automatically segment the video streams into shots. Finally, a self-attention model is introduced to select key-frames sequences inside the shots; thus, key static images are selected as video content summarization, and optical flows can be calculated as video motion summarization. The ultimate experimental results demonstrate that our method is competitive on VSUMM, TvSum, SumMe, and RAI datasets about shot segmentation and video content summarization, and can also represent a good motion summarization result.
机译：视频摘要（VSUMM）已成为处理大规模视频数据的流行方法。 Vsumm的关键点是选择关键帧以表示视频序列的有效内容。现有方法只能提取视频的静态图像作为内容摘要，但它们忽略了运动信息的表示。为了应对这些问题，提出了一种用于高效视频内容摘要的新框架以及视频运动摘要。最初，胶囊网被训练为时空信息提取器，并且基于那些时空特征产生帧间运动曲线。随后，提出过渡效果检测方法以自动将视频流分段为拍摄。最后，引入了自我关注模型，以选择镜头内的键框架序列;因此，选择密钥静态图像作为视频内容摘要，并且可以计算光学流量作为视频运动摘要。最终的实验结果表明，我们的方法对拍摄分割和视频内容摘要的VSUMM，TVSUM，SUMME和RAI数据集具有竞争力，并且还可以代表良好的运动摘要结果。

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