Fast-Forward Video Based on Semantic Extraction

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.

Accepted for publication and presented in 2016 IEEE International Conference on Image Processing (ICIP)

References in corpus (2)

• A Fast and Accurate Unconstrained Face Detector
• EgoSampling: Fast-Forward and Stereo for Egocentric Videos

Cited by in corpus (6)

• A Weighted Sparse Sampling and Smoothing Frame Transition Approach for Semantic Fast-Forward First-Person Videos
• Making a long story short: A Multi-Importance fast-forwarding egocentric videos with the emphasis on relevant objects
• Towards Semantic Fast-Forward and Stabilized Egocentric Videos
• A Sparse Sampling-based framework for Semantic Fast-Forward of First-Person Videos
• Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method
• Collaborative Multi-Agent Video Fast-Forwarding