# Towards Semantic Fast-Forward and Stabilized Egocentric Videos

arXiv: https://arxiv.org/abs/1708.04146 · HTML: https://mlsanity.com/paper/1708.04146/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [Collaborative Multi-Agent Video Fast-Forwarding](https://mlsanity.com/paper/2305.17569/) (2023)
- [Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method](https://mlsanity.com/paper/2203.15778/) (2022)

## Abstract (arXiv)

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.