Fast-Forward Video Based on Semantic Extraction

TL;DR

This paper introduces a novel method for creating fast-forward egocentric videos by selecting frames based on semantic content, resulting in more engaging and pleasant viewing experiences.

Contribution

The proposed approach leverages semantic information to improve frame selection in fast-forward videos, outperforming existing methods in semantic relevance and viewer satisfaction.

Findings

Outperforms state-of-the-art in semantic relevance

Produces more pleasant and engaging videos

Effective frame selection based on semantic content

Abstract

Thanks to the low operational cost and large storage capacity of smartphones and wearable devices, people are recording many hours of daily activities, sport actions and home videos. These videos, also known as egocentric videos, are generally long-running streams with unedited content, which make them boring and visually unpalatable, bringing up the challenge to make egocentric videos more appealing. In this work we propose a novel methodology to compose the new fast-forward video by selecting frames based on semantic information extracted from images. The experiments show that our approach outperforms the state-of-the-art as far as semantic information is concerned and that it is also able to produce videos that are more pleasant to be watched.