Making a long story short: A Multi-Importance fast-forwarding egocentric videos with the emphasis on relevant objects

The emergence of low-cost, high-quality personal wearable cameras combined with the increasing storage capacity of video-sharing websites have evoked a growing interest in first-person videos. Since most videos are composed of long-running unedited streams which are usually tedious and unpleasant to watch. State-of-the-art fast-forward methods currently face the challenge of providing an adequate balance between smoothness in visual flow and the emphasis on the relevant parts. In this work, we present the Multi-Importance Fast-Forward (MIFF), a fully automatic methodology to fast-forward egocentric videos facing these challenges. The dilemma of defining what is the semantic information of a video is addressed by a learning process based on the preferences of the user. Results show that the proposed method keeps over 3 times more semantic content than the state-of-the-art fast-forward. Finally, we discuss the need of a particular video stabilization techniques for fast-forward egocentric videos.

@article{Silva2018,
title = {Making a long story short: A Multi-Importance fast-forwarding egocentric videos with the emphasis on relevant objects},
author = {Michel M. Silva and Washington L. S. Ramos and Felipe C. Chamone and João P. K. Ferreira and Mario F. M. Campos and Erickson R. Nascimento},
journal = {Journal of Visual Communication and Image Representation},
volume = {53},
number = {},
pages = {55 – 64},
year = {2018},
issn = {1047-3203},
doi = {10.1016/j.jvcir.2018.02.013}
}