Thanks to the advances in the technology of low-cost digital cameras and the popularity of the self-recording culture, the amount of visual data on the Internet is going to the opposite side of the available time and patience of the users. Thus, most of the uploaded videos are doomed to be forgotten and unwatched in a computer folder or website. In this work, 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, which combined with a smoothing frame transition method accelerates first-person videos emphasizing the relevant segments and avoids visual discontinuities. The experiments show that our method is able to fast-forward videos to retain as much relevant information and smoothness as the state-of-the-art techniques in less time. We also present a new 80-hour multimodal (RGB-D, IMU, and GPS) dataset of first-person videos with annotations for recorder profile, frame scene, activities, interaction, and attention.

该研究提出了一种新的自适应帧选择方法和平滑帧转换方法以加速第一人称视频并强调相关段落，从而避免视觉不连续性，实验表明我们的方法能够在更短的时间内保留尽可能多的相关信息和平滑度，并且我们还提供了一个新的80小时的多模态数据集，包含了记录者资料、帧场景、活动、互动和注意力的注释。

一种加权稀疏采样和平滑过渡的语义快进第一人称视频方法