The emergence of low-cost personal mobiles devices and wearable cameras and the increasing storage capacity of video-sharing websites have pushed forward a growing interest towards first-person videos. Since most of the recorded videos compose long-running streams with unedited content, they are tedious and unpleasant to watch. The fast-forward state-of-the-art methods are facing challenges of balancing the smoothness of the video and the emphasis in the relevant frames given a speed-up rate. In this work, we present a methodology capable of summarizing and stabilizing egocentric videos by extracting the semantic information from the frames. This paper also describes a dataset collection with several semantically labeled videos and introduces a new smoothness evaluation metric for egocentric videos that is used to test our method.
@article{arxiv.1708.04146,
title = {Towards Semantic Fast-Forward and Stabilized Egocentric Videos},
author = {Michel Melo Silva and Washington Luis Souza Ramos and Joao Pedro Klock Ferreira and Mario Fernando Montenegro Campos and Erickson Rangel Nascimento},
journal= {arXiv preprint arXiv:1708.04146},
year = {2017}
}
Comments
Accepted for publication and presented in the First International Workshop on Egocentric Perception, Interaction and Computing at European Conference on Computer Vision (EPIC@ECCV) 2016