Technological advances in sensors have paved the way for digital cameras to become increasingly ubiquitous, which, in turn, led to the popularity of the self-recording culture. As a result, the amount of visual data on the Internet is moving in the opposite direction of the available time and patience of the users. Thus, most of the uploaded videos are doomed to be forgotten and unwatched stashed away in some computer folder or website. In this paper, we address the problem of creating smooth fast-forward videos without losing the relevant content. We present a new adaptive frame selection formulated as a weighted minimum reconstruction problem. Using a smoothing frame transition and filling visual gaps between segments, our approach accelerates first-person videos emphasizing the relevant segments and avoids visual discontinuities. Experiments conducted on controlled videos and also on an unconstrained dataset of First-Person Videos (FPVs) show that, when creating fast-forward videos, our method is able to retain as much relevant information and smoothness as the state-of-the-art techniques, but in less processing time.

中文翻译：

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一种基于稀疏采样的第一人称视频语义快进框架

传感器的技术进步为数码相机变得越来越无处不在铺平了道路，这反过来又导致了自拍文化的流行。结果，互联网上的视觉数据量正朝着与用户可用时间和耐心相反的方向发展。因此，大多数上传的视频注定会被遗忘和隐藏在某些计算机文件夹或网站中。在本文中，我们解决了在不丢失相关内容的情况下创建流畅的快进视频的问题。我们提出了一种新的自适应帧选择，它被表述为加权最小重建问题。使用平滑的帧过渡和填充片段之间的视觉间隙，我们的方法可以加速强调相关片段的第一人称视频并避免视觉不连续。