Making a long story short: A Multi-Importance fast-forwarding egocentric videos with the emphasis on relevant objects

Abstract

The emergence of low-cost high-quality personal wearable cameras combined with the increasing storage capacity of video-sharing websites have evoked a growing interest in first-person videos, since most videos are composed of long-running unedited streams which are usually tedious and unpleasant to watch. State-of-the-art semantic fast-forward methods currently face the challenge of providing an adequate balance between smoothness in visual flow and the emphasis on the relevant parts. In this work, we present the Multi-Importance Fast-Forward (MIFF), a fully automatic methodology to fast-forward egocentric videos facing these challenges. The dilemma of defining what is the semantic information of a video is addressed by a learning process based on the preferences of the user. Results show that the proposed method keeps over $3$ times more semantic content than the state-of-the-art fast-forward. Finally, we discuss the need of a particular video stabilization technique for fast-forward egocentric videos.

Accepted to publication in the Journal of Visual Communication and Image Representation (JVCI) 2018. Project website: https://www.verlab.dcc.ufmg.br/semantic-hyperlapse

References in corpus (3)

• Video Summarization with Long Short-term Memory
• EgoSampling: Wide View Hyperlapse from Egocentric Videos
• Fast-Forward Video Based on Semantic Extraction

Cited by in corpus (5)

• A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos
• A Sparse Sampling-based framework for Semantic Fast-Forward of First-Person Videos
• Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
• Fast forwarding Egocentric Videos by Listening and Watching
• A gaze driven fast-forward method for first-person videos