Discovering Picturesque Highlights from Egocentric Vacation Videos
This addresses the challenge of efficiently curating highlights from personal vacation videos for users, though it is incremental in applying aesthetic analysis to egocentric data.
The paper tackles the problem of automatically identifying picturesque highlights from large amounts of egocentric vacation videos by analyzing aesthetic features like composition, symmetry, and color vibrancy, and demonstrates results on a new dataset of 26.5 hours of videos over 14 days with user-study validation.
We present an approach for identifying picturesque highlights from large amounts of egocentric video data. Given a set of egocentric videos captured over the course of a vacation, our method analyzes the videos and looks for images that have good picturesque and artistic properties. We introduce novel techniques to automatically determine aesthetic features such as composition, symmetry and color vibrancy in egocentric videos and rank the video frames based on their photographic qualities to generate highlights. Our approach also uses contextual information such as GPS, when available, to assess the relative importance of each geographic location where the vacation videos were shot. Furthermore, we specifically leverage the properties of egocentric videos to improve our highlight detection. We demonstrate results on a new egocentric vacation dataset which includes 26.5 hours of videos taken over a 14 day vacation that spans many famous tourist destinations and also provide results from a user-study to access our results.