First International Workshop on Egocentric Perception, Interaction and Computing at European Conference on Computer Vision (EPIC@ECCV) 2016
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.
Keywords: Semantic Information, First-person Video, Fast-Forward, Egocentric Stabilization
Methodology and Results.
We compare this proposed methodology against the following methods:
- EgoSampling – Poleg et al., Egosampling: Fast-forward and stereo for egocentric videos, CVPR 2015.
- Microsoft Hyperlapse – Joshi et al., Real-time hyperlapse creation via optimal frame selection, ACM. Trans. Graph. 2015.
- Fast-Forward based on Semantic Extraction – Ramos et al., Fast-forward video based on semantic extraction, ICIP 2016.
We conducted the experimental evaluation using the datasets:
- Semantic dataset – Silva et al., Towards Semantic Fast-Forward and Stabilized Egocentric Videos, EPIC 2016.
João Pedro Klock FerreiraUndergraduate Student