  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.
