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A video summarization approach based on the emulation of bottom-up mechanisms of visual attention

机译：一种基于视觉自下而上机制的视频摘要方法

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This work addresses the development of a computational model of visual attention to perform the automatic summarization of digital videos from television archives. Although the television system represents one of the most fascinating media phenomena ever created, we still observe the absence of effective solutions for content-based information retrieval from video recordings of programs produced by this media universe. This fact relates to the high complexity of the content-based video retrieval problem, which involves several challenges, among which we may highlight the usual demand on video summaries to facilitate indexing, browsing and retrieval operations. To achieve this goal, we propose a new computational visual attention model, inspired on the human visual system and based on computer vision methods (face detection, motion estimation and saliency map computation), to estimate static video abstracts, that is, collections of salient images or key frames extracted from the original videos. Experimental results with videos from the Open Video Project show that our approach represents an effective solution to the problem of automatic video summarization, producing video summaries with similar quality to the ground-truth manually created by a group of 50 users.
机译：这项工作解决了视觉注意力计算模型的发展，以执行电视档案中数字视频的自动汇总。尽管电视系统代表了有史以来最令人着迷的媒体现象之一，但我们仍然观察到缺乏有效的解决方案，无法从该媒体世界制作的节目的录像中获取基于内容的信息。这个事实与基于内容的视频检索问题的高度复杂性有关，它涉及几个挑战，其中我们可能会突出显示通常对视频摘要的需求，以促进索引，浏览和检索操作。为了实现此目标，我们提出了一种新的计算视觉注意力模型，该模型受到人类视觉系统的启发，并基于计算机视觉方法（面部检测，运动估计和显着性图计算）来估计静态视频摘要，即显着集合从原始视频中提取的图像或关键帧。来自Open Video Project的视频的实验结果表明，我们的方法代表了自动视频摘要问题的有效解决方案，可以产生质量与由50个用户组手动创建的真实情况类似的视频摘要。

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