A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos

TL;DR

This paper introduces a novel adaptive frame selection and smoothing method for creating smooth, relevant-content-preserving fast-forward first-person videos, outperforming existing techniques in efficiency and quality.

Contribution

It proposes a weighted sparse sampling and smoothing framework that accelerates first-person videos while maintaining content relevance and visual smoothness, supported by a new multimodal dataset.

Findings

Retains relevant information effectively

Achieves smoother transitions in fast-forward videos

Operates faster than state-of-the-art methods

Abstract

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.