Sixth International Workshop on Egocentric Perception, Interaction and Computing at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (EPIC@CVPR) 2020

Visit the workshop page.

Abstract

The growing data sharing and life-logging cultures are driving an unprecedented increase in the amount of unedited First-Person Videos. In this paper, we address the problem of accessing relevant information in First-Person Videos by creating an accelerated version of the input video and emphasizing the important moments to the recorder. Our method is based on an attention model driven by gaze and visual scene analysis that provides a semantic score of each frame of the input video. We performed several experimental evaluations on publicly available First-Person Videos datasets. The results show that our methodology can fast-forward videos emphasizing moments when the recorder visually interact with scene components while not including monotonous clips.

Methodology

Links

Source code (Coming Soon!)

ArXiv (NEW!)

Supplementary Video with Visual Results

Citation

@InProceedings{Neves2020epic@cvpr,
title = {A gaze driven fast-forward method for first-person videos},
booktitle = {Sixth International Workshop on Egocentric Perception, Interaction and Computing at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (EPIC@CVPR)},
author = {Alan Neves, Michel Silva, Mario Campos, Erickson R. Nascimento},
Year = {2020},
month = {Jun.},
pages = {1-4}
}

Baselines

We compare the proposed methodology against the following methods:

Datasets

We conducted the experimental evaluation using the following datasets:

Authors


Alan Carvalho Neves

MSc Student

Back to the project page.