Towards Semantic Fast-Forward and Stabilized Egocentric Videos

TL;DR

This paper proposes a method to summarize and stabilize egocentric videos by extracting semantic information, balancing smoothness and relevance, and introduces a new dataset and evaluation metric for this purpose.

Contribution

It introduces a novel approach for semantic-based fast-forwarding and stabilization of egocentric videos, along with a new dataset and evaluation metric.

Findings

Effective summarization and stabilization of egocentric videos.

New dataset with semantically labeled videos.

A novel smoothness evaluation metric.

Abstract

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.