A Sparse Sampling-based framework for Semantic Fast-Forward of First-Person Videos
This addresses the challenge of efficiently summarizing large volumes of first-person videos for users with limited time, though it is incremental as it builds on existing fast-forward techniques.
The paper tackles the problem of creating smooth fast-forward videos from first-person footage without losing relevant content, presenting a sparse sampling-based framework that achieves comparable information retention and smoothness to state-of-the-art methods but with reduced processing time.
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.